From 1569adcffba1ad84f3bab25c59684d0d19e6c3d4 Mon Sep 17 00:00:00 2001 From: Liao Wenzhe <13253595285@163.com> Date: Mon, 11 Apr 2022 10:22:00 +0800 Subject: [PATCH] Reinitialize --- .idea/.gitignore | 8 + .idea/dataRisk-detection-resources.iml | 8 + .idea/inspectionProfiles/Project_Default.xml | 25 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + .idea/vcs.xml | 6 + LICENSE | 661 +++++++++++++++ README.md | 293 +++++++ README.rst | 758 ++++++++++++++++++ README_CN.rst | 553 +++++++++++++ download.py | 32 + resource_urls/1 | 1 + resource_urls/papers.txt | 6 + url_checker.py | 47 ++ 15 files changed, 2416 insertions(+) create mode 100644 .idea/.gitignore create mode 100644 .idea/dataRisk-detection-resources.iml create mode 100644 .idea/inspectionProfiles/Project_Default.xml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml create mode 100644 LICENSE create mode 100644 README.md create mode 100644 README.rst create mode 100644 README_CN.rst create mode 100644 download.py create mode 100644 resource_urls/1 create mode 100644 resource_urls/papers.txt create mode 100644 url_checker.py diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..73f69e0 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml +# Editor-based HTTP Client requests +/httpRequests/ diff --git a/.idea/dataRisk-detection-resources.iml b/.idea/dataRisk-detection-resources.iml new file mode 100644 index 0000000..d0876a7 --- /dev/null +++ b/.idea/dataRisk-detection-resources.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..0f92108 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,25 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..c3334de --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..d0e9274 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..94a25f7 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..dbbe355 --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU Affero General Public License as published + by the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU Affero General Public License for more details. + + You should have received a copy of the GNU Affero General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..318ab2b --- /dev/null +++ b/README.md @@ -0,0 +1,293 @@ + +# dataRisk-detection-resources(数据安全智能风控学习资源) +随着2021年中国《数据安全保护法》发布，意味着数据安全有望在中国形成新的风口.
+ +笔者有幸在2021年加入中国领先的数据安全创业公司之一，从事数据科学结合数据安全的前沿研究和落地工作。在探索过程中发现网络上专门针对数据安全的资料不多，遂诞生了自己整理相关资料和思考的想法，希望尽自己的微薄之力推动社区发展。
+ + +**拒绝白嫖，欢迎star！**

+笔者组建了个大数据安全技术交流的群，群友遍布硅谷，新加坡，腾讯，阿里，浙大等等，欢迎志同道合的朋友与我联系加入！ + +最近更新日期为：2022/1

+## 入门综述 + +- [为什么机器学习解决网络安全问题总是失败：谈谈特征空间](https://toooold.com/2021/09/27/why_ml_fails_solving_sec_algo.html) +- [为什么机器学习解决网络安全问题总是失败：脆弱的系统工程](https://toooold.com/2021/10/11/why_ml_fails_security_frag_cn.html) +- [为什么机器学习解决网络安全问题总是失败：不合理的评估指标](https://toooold.com/2021/11/13/why_ml_fails_security_evaluation_cn.html) +- [为什么机器学习解决网络安全问题总是失败：机器学习不是万能灵药](https://toooold.com/2021/11/28/why_ml_fails_security_ml_is_not_everything_cn.html) + +## AI应用防御篇 +用AI来做应用安全防护 + +### OWASP10 +- https://salt.security/blog/what-is-the-owasp-api-security-top-10? + +### API风险发现系统 +- https://mp.weixin.qq.com/s/-9xkAROp7_A6gDjTLxfUsg + +### 风险业务 +- https://mp.weixin.qq.com/s/H9CoDtII37dKJYJ9HleY6w +- https://mp.weixin.qq.com/s/xGY1PxoH9Tlio2mWH7QLjw +- [杜跃进：数据安全治理的基本思路](https://www.secrss.com/articles/6420) +- [api安全治理思路](https://mp.weixin.qq.com/s/Q9fZrq51fLNf0itPj1p9Vw) +- 数据安全复合治理与实践白皮书 +- [参数篡改与流量重放](https://juejin.cn/post/6890798533473992717) +- [常见API攻击](https://zhuanlan.zhihu.com/p/472101085) +- [salt blog](https://salt.security/blog) +- [imperva blog](https://www.imperva.com/blog/) + + +### 恶意注册账户 +- 《Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach》 +- 《DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks》 +- https://zhuanlan.zhihu.com/p/59666737 + + +### marchine learning for UEBA +- 《AI2: Training a big data machine to defend》 +- 《Big Data Security Challenges: An Overview and Application of User Behavior Analytics》 +- 《Adaptive Intrusion Detection System via Online Learning》 +- 《A multi-model approach to the detection of web-based attacks》 +- 《McPAD : A Multiple Classifier System for Accurate Payload-based Anomaly Detection》 +- 《Using Generalization and Characterization Techniques in the Anomaly-based Detection of Web Attacks》 +- 《Anomaly-Based Web Attack Detection: A Deep Learning Approach》 +- 《A Big Data Analysis Framework for Model-Based Web User Behavior Analytics》 +- 《Anomalous Payload-based Network Intrusion Detection》 +- 《Data mining for security at Google》 +- 《User and Entity Behavior Analytics for Enterprise Security》 +- 《A Comprehensive Approach to Intrusion Detection Alert Correlation》 +- 《Trafc Anomaly Detection Using K-Means Clustering》 +- 《Calculation of the Behavior Utility of a Network System: Conception and Principle》 +- 《Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic》 +- 《用户画像相关技术》 + + +### MLOPS +- https://mp.weixin.qq.com/s/rdOqndedCSs926GiQRs2Rg + +### 入侵检测 +- https://blog.cloudflare.com/api-abuse-detection/ +- [利用机器学习检测HTTP恶意外连流量](https://www.freebuf.com/column/170483.html) +- [ExecScent: Mining for New C&C Domains in Live Networks with Adaptive Control Protocol Templates](https://www.usenix.org/system/files/conference/usenixsecurity13/sec13-paper_nelms.pdf) +- [MADE: Security Analytics for Enterprise Threat Detection](http://www.ccs.neu.edu/home/alina/papers/MADE.pdf) +- [机器学习在互联网巨头公司实践](https://mp.weixin.qq.com/s/NFqUF824Rpr4g6wYWFpSNQ) +- [机器学习在入侵检测方面的应用 - 基于ADFA-LD训练集训练入侵检测判别模型](https://www.cnblogs.com/LittleHann/p/7806093.html#_lab2_0_1) +- [datacon比赛方向三-攻击源与攻击者分析writeup](https://github.com/ReAbout/datacon) +- [基于机器学习的恶意软件加密流量检测研究分享](https://blog.riskivy.com/%e5%9f%ba%e4%ba%8e%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e7%9a%84%e6%81%b6%e6%84%8f%e8%bd%af%e4%bb%b6%e5%8a%a0%e5%af%86%e6%b5%81%e9%87%8f%e6%a3%80%e6%b5%8b/?from=groupmessage&isappinstalled=0) +- [anomaly-detection-through-reinforcement-learning](https://zighra.com/blogs/anomaly-detection-through-reinforcement-learning/) + +### 恶意url检测 + +- [URLNet：通过深度学习学习URL表示以进行恶意URL检测](https://arxiv.org/abs/1802.03162v2) +- [我的AI安全检测学习笔记（一）](http://4o4notfound.org/index.php/archives/127/) +- 《Compromised or Attacker-Owned: A Large Scale Classification and Study of Hosting Domains of Malicious URLs》 + +### DDOS + +- [基于KDDCUP 99数据集预测DDoS攻击](https://github.com/aviraonepiece/machine_learning) +- [基于谱分析与统计机器学习的DDoS攻击检测技术研究](http://wap.cnki.net/lunwen-1013353778.html) +- [基于机器学习的分布式拒绝服务攻击检测方法研究](http://cdmd.cnki.com.cn/Article/CDMD-90002-2007140546.htm) +- [DDoS Attacks Using Hidden Markov Models and Cooperative ReinforcementLearning*](https://pdfs.semanticscholar.org/6363/b9f28a7e037abe626a2e88fac3393c04bfda.pdfDefending ) + + +### 僵尸网络检测 +- [Win the 0-Day Racing Game Against Botnet on Cloud](https://i.blackhat.com/asia-20/Friday/asia-20-Xu-Win-The-0-Day-Racing-Game-Against-Botnet-In-Public-Cloud.pdf) +- [datacon 2020 僵尸网络检测](https://zhuanlan.zhihu.com/p/186254809) + +## dga域名检测 +- [https://www.secrss.com/articles/14369] +- [https://www.cnblogs.com/networking/p/14788479.html] + + +### 机器人流量识别 +- https://blog.cloudflare.com/api-abuse-detection/ + +### 爬虫识别 +- https://blog.csdn.net/xiao_yi_xiao/article/details/101835176 +- https://blog.csdn.net/qq_25834767/article/details/103546251 + +### 应用日志与数据库日志IP-API-SQl三层关联支撑风险发现 + +### Web安全异常检测 ### +- [LSTM识别恶意HTTP请求](https://www.cdxy.me/?p=775) +- [基于URL异常检测的机器学习模型mini部署](http://4o4notfound.org/index.php/archives/84/) +- [我的AI安全检测学习笔记（一）](http://4o4notfound.org/index.php/archives/127/) +- [基于机器学习的WEB攻击分类检测模型](https://www.freebuf.com/news/184687.html) +- [基于机器学习的攻击检测系统](https://www.freebuf.com/column/189981.html) +- [WAF建设运营及AI应用实践](https://mp.weixin.qq.com/s/fTm1hUfRmm6ujmjvSHRLUA) +- [Web安全检测中机器学习的经验之谈](https://iami.xyz/ML-IN-Webshell-Detection-Advantages-And-Disadvantages/) +- [APT detection based on machine learning](https://mp.weixin.qq.com/s?__biz=MzU5MTM5MTQ2MA==&mid=2247484139&idx=1&sn=0da63a49f341eccc0bb48c954d8ebbb4&chksm=fe2efd60c95974767521fe6a6b7257a1d05e5482fc7ddeda281bdf0f0deb20add82d1a82d8ec&mpshare=1&scene=1&srcid=&pass_ticket=bjnNiDKomd79pQvRonW%2BXsTe6JrO%2FFs6oII12dZaLBPuQOtNK6Rzh9WSJ%2B%2F89ZUA#rd) +- [RSAC 2019 | 机器学习算法分析引擎助力安全威胁推理分析](http://blog.nsfocus.net/machine-learning-algorithms-analysis-engine-security-threat-reasoning/) +- [解决机器学习和安全运营之间的最后一公里问题](https://www.anquanke.com/post/id/163637) +- [RSAC 2019 | 采用NLP机器学习来进行自动化合规风险治理](http://blog.nsfocus.net/automated-compliance-risk-management-nlp-machine-learning/) + +### 渗透测试入门 ### +- [dvwa闯关教程](https://www.freebuf.com/articles/web/274058.html) + +## 数据集 + +1、[Samples of Security Related Dats](http://link.zhihu.com/?target=http%3A//www.secrepo.com/) + +2、[DARPA Intrusion Detection Data Sets](http://link.zhihu.com/?target=https%3A//www.ll.mit.edu/ideval/data/) + +3、[Stratosphere IPS Data Sets](http://link.zhihu.com/?target=https%3A//stratosphereips.org/category/dataset.html) + +4、[Open Data Sets](http://link.zhihu.com/?target=http%3A//csr.lanl.gov/data/) + +5、[Data Capture from National Security Agency](http://link.zhihu.com/?target=http%3A//www.westpoint.edu/crc/SitePages/DataSets.aspx) + +6、[The ADFA Intrusion Detection Data Sets](http://link.zhihu.com/?target=https%3A//www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-IDS-Datasets/) + +7、[NSL-KDD Data Sets](http://link.zhihu.com/?target=https%3A//github.com/defcom17/NSL_KDD) + +8、[Malicious URLs Data Sets](http://link.zhihu.com/?target=http%3A//sysnet.ucsd.edu/projects/url/) + +9、[Multi-Source Cyber-Security Events](http://link.zhihu.com/?target=http%3A//csr.lanl.gov/data/cyber1/) + +10、[Malware Training Sets: A machine learning dataset for everyone](http://link.zhihu.com/?target=http%3A//marcoramilli.blogspot.cz/2016/12/malware-training-sets-machine-learning.html) + +11. [Collection of Security and Network Data Resources](http://www.covert.io/data-links/) +12. http://www.secrepo.com/ + +13. [Vulnbank_dataset](https://github.com/AnchoretY/AI_And_Web_Security_Library/tree/master/dataset/vulnbank_dataset). KDD大赛的一个竞赛项目，主要目的是使用机器学习得手段建立一个入侵检测器。其中的入侵行为主要包括：DDOS、密码暴力破解、缓冲区溢出、扫描等多种攻击行为。 + +## 优秀开源推荐 +- https://github.com/LiaoWenzhe +- https://github.com/yzhao062/pyod +- https://github.com/yzhao062/anomaly-detection-resources +- [网络安全中机器学习大合集](https://github.com/jivoi/awesome-ml-for-cybersecurity/blob/master/README_ch.md) +- [最终安全数据科学和机器学习指南](http://www.covert.io/the-definitive-security-datascience-and-machinelearning-guide/) +- [Machine Learning for Cyber Security](https://github.com/wtsxDev/Machine-Learning-for-Cyber-Security#-datasets) +- [404师傅的整理](https://github.com/404notf0und/AI-for-Security-Learning) +- [Awesome-AI-Security](https://github.com/RandomAdversary/Awesome-AI-Security) +- [awesome-ml-for-cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity#-datasets) +- [The Definitive Security Data Science and Machine Learning Guide](http://www.covert.io/the-definitive-security-datascience-and-machinelearning-guide/) +- https://github.com/0xMJ/AI-Security-Learning +- [乌云](https://wooyun.x10sec.org/search?keywords=aa&content_search_by=by_bugs) + +## 思维方式： +- [提出好的想法和方向](https://mp.weixin.qq.com/s/jajNXjNxfAvV-7SmLnVUAQ) +- [刘知远：好的研究想法从哪里来](https://zhuanlan.zhihu.com/p/93765082) +- [MIT人工智能实验室：如何做研究](https://blog.csdn.net/jinjinstudy/article/details/9413213) + + +## 实用工具 +- [ReadPaper论文阅读平台](https://mp.weixin.qq.com/s/iT3cCqw59707iN4-ChorFA) +- arxiv +- google scholar +- [百度rasp安全检测工具](https://rasp.baidu.com/doc/) + +## 优秀公众号 +- 安全学术圈 +- 阿里安全应急响应中心 +- 腾讯安全应急响应中心 +- 百度安全应急响应中心 +- dataFunTalk +- freebuf +- 404 Not F0und + +## 相关顶会 +- BlackHat / BlackHat Asia +- owasp +- botconf +- DEF-CON +- S&P +- CCS +- ICDFC +- USENIX Security +- PETS +- Wisec +- CODASPY +- ICSE +- NDSS +- Computer & Security +- TDSC +- RSAC + +## 相关公司 +- 全知科技 +- salt +- 绿盟科技 +- 安恒信息 +- 闪捷信息 +- 奇安信 +- imperva + +## 相关赛事 +- DataCon +- DataFountain + +## 优秀书籍 +- 《风控要略：互联网业务反欺诈之路》 +- 《web安全之机器学习入门》 +- 《web安全之深度学习实战》 +- 《web安全之强化学习与Gan》 +- 《白帽子讲web安全》 +- 《图解http》 + +## 相关博客 +- https://blog.csdn.net/Liao_Wenzhe/ +- http://iami.xyz +- https://www.cdxy.me/ +- https://blog.netlab.360.com/ +- 阿里云安全 + + +## BlackHat 上一些有意思的web攻防演讲 +- https://www.blackhat.com/docs/asia-17/materials/asia-17-Dong-Beyond-The-Blacklists-Detecting-Malicious-URL-Through-Machine-Learning.pdf +- https://i.blackhat.com/briefings/asia/2018/asia-18-Simakov-Marina-Breaking-The-Attack-Graph.pdf +- https://i.blackhat.com/asia-19/Fri-March-29/bh-asia-Pham-Automated-REST-API-Endpoint.pdf +- https://i.blackhat.com/asia-20/Friday/asia-20-Hao-Attacking-And-Defending-Machine-Learning-Applications-Of-Public-Cloud.pdf +- https://i.blackhat.com/eu-19/Wednesday/eu-19-Kettle-HTTP-Desync-Attacks-Request-Smuggling-Reborn.pdf +- https://www.blackhat.com/docs/us-17/wednesday/us-17-Gil-Web-Cache-Deception-Attack.pdf +- https://www.blackhat.com/docs/us-17/wednesday/us-17-Burnett-Ichthyology-Phishing-As-A-Science-wp.pdf +- https://i.blackhat.com/us-18/Thu-August-9/us-18-Kettle-Practical-Web-Cache-Poisoning-Redefining-Unexploitable.pdf +- https://i.blackhat.com/us-18/Thu-August-9/us-18-Kettle-Practical-Web-Cache-Poisoning-Redefining-Unexploitable.pdf +- https://i.blackhat.com/USA-19/Wednesday/us-19-Valenta-Monsters-In-The-Middleboxes-Building-Tools-For-Detecting-HTTPS-Interception.pdf +- https://i.blackhat.com/USA-20/Wednesday/us-20-Kettle-Web-Cache-Entanglement-Novel-Pathways-To-Poisoning.pdf +- https://www.163.com/dy/article/GPJBLI020511CJ6O.html +- https://i.blackhat.com/USA-20/Wednesday/us-20-Klein-HTTP-Request-Smuggling-In-2020-New-Variants-New-Defenses-And-New-Challenges.pdf +- https://i.blackhat.com/EU-21/Wednesday/EU-21-Thatcher-Practical-HTTP-Header-Smuggling.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Gavrichenkov-Breaking-HTTPS-With-BGP-Hijacking-wp.pdf +- https://www.blackhat.com/docs/us-16/materials/us-16-Sivakorn-HTTP-Cookie-Hijacking-In-The-Wild-Security-And-Privacy-Implications-wp.pdf +- https://towardsdatascience.com/deep-learning-for-specific-information-extraction-from-unstructured-texts-12c5b9dceada +- https://portswigger.net/research/cracking-the-lens-targeting-https-hidden-attack-surface +- https://www.botconf.eu/category/keynote/ +- https://www.botconf.eu/2016/getting-your-hands-dirty-how-to-analyze-the-behavior-of-malware-traffic-and-web-connections/ +- https://www.botconf.eu/2015/dga-clustering-and-analysis-mastering-modern-evolving-threats/ +- https://www.blackhat.com/us-16/briefings/schedule/#account-jumping-post-infection-persistency--lateral-movement-in-aws-4309 +- https://www.blackhat.com/us-16/briefings/schedule/#http-cookie-hijacking-in-the-wild-security-and-privacy-implications-3467 +- https://www.blackhat.com/docs/us-17/wednesday/us-17-Kettle-Cracking-The-Lens-Exploiting-HTTPs-Hidden-Attack-Surface.pdf +- https://www.blackhat.com/docs/us-17/wednesday/us-17-Kettle-Cracking-The-Lens-Exploiting-HTTPs-Hidden-Attack-Surface-wp.pdf +- https://www.blackhat.com/docs/us-17/thursday/us-17-Prandl-PEIMA-Harnessing-Power-Laws-To-Detect-Malicious-Activities-From-Denial-Of-Service-To-Intrusion-Detection-Traffic-Analysis-And-Beyond.pdf +- https://www.blackhat.com/docs/us-17/thursday/us-17-Prandl-PEIMA-Harnessing-Power-Laws-To-Detect-Malicious-Activities-From-Denial-Of-Service-To-Intrusion-Detection-Traffic-Analysis-And-Beyond-wp.pdf +- https://www.blackhat.com/docs/us-17/thursday/us-17-Hypponen-The-Epocholypse-2038-Whats-In-Store-For-The-Next-20-Years.pdf +- https://www.blackhat.com/docs/us-16/materials/us-16-Amiga-Account-Jumping-Post-Infection-Persistency-And-Lateral-Movement-In-AWS.pdf +- https://www.blackhat.com/docs/us-16/materials/us-16-Sivakorn-HTTP-Cookie-Hijacking-In-The-Wild-Security-And-Privacy-Implications.pdf +- https://www.blackhat.com/docs/us-16/materials/us-16-Gelernter-Timing-Attacks-Have-Never-Been-So-Practical-Advanced-Cross-Site-Search-Attacks.pdf +- https://www.blackhat.com/docs/us-16/materials/us-16-Ermishkin-Viral-Video-Exploiting-Ssrf-In-Video-Converters.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Gavrichenkov-Breaking-HTTPS-With-BGP-Hijacking.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-Detection-Machine-Learning-And-The-SOC.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Zadeh-From-False-Positives-To-Actionable-Analysis-Behavioral-Intrusion-Detection-Machine-Learning-And-The-SOC-wp.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Morgan-Web-Timing-Attacks-Made-Practical.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Morgan-Web-Timing-Attacks-Made-Practical-wp.pdf +- https://www.blackhat.com/docs/us-15/materials/us-15-Saxe-Why-Security-Data-Science-Matters-And-How-Its-Different.pdf +- https://www.blackhat.com/docs/us-14/materials/us-14-Pinto-Secure-Because-Math-A-Deep-Dive-On-Machine-Learning-Based-Monitoring-WP.pdf +- https://www.blackhat.com/docs/us-14/materials/us-14-Pinto-Secure-Because-Math-A-Deep-Dive-On-Machine-Learning-Based-Monitoring.pdf +- https://media.blackhat.com/us-13/US-13-Pinto-Defending-Networks-with-Incomplete-Information-A-Machine-Learning-Approach-Slides.pdf +- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2013/US-13-Pinto-Defending-Networks-with-Incomplete-Information-A-Machine-Learning-Approach-Slides.pdf +- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2013/US-13-Peck-Abusing-Web-APIs-Through-Scripted-Android-Applications-WP.pdf +- https://www.youtube.com/watch?v=RGqCZO3cgY8 +- https://www.youtube.com/watch?v=JUY4DQZ02o4 +- https://www.youtube.com/watch?v=D6MG2uBIfUI +- https://paper.bobylive.com/Meeting_Papers/BlackHat/USA-2011/BH_US_11_Balduzzi_HPP_Slides.pdf +- https://www.blackhat.com/html/bh-us-11/bh-us-11-archives.html +- https://www.blackhat.com/docs/eu-14/materials/eu-14-Hafif-Reflected-File-Download-A-New-Web-Attack-Vector.pdf +- https://www.madlab.it/slides/BHEU2011/whitepaper-bhEU2011.pdf +- https://infocon.org/cons/Black%20Hat/Black%20Hat%20Europe/Black%20Hat%20Europe%202011/Presentations/Raul_Siles/BlackHat_EU_2011_Siles_SAP_Session-WP.pdf +- https://www.blackhat.com/presentations/bh-europe-09/Zanero_Criscione/BlackHat-Europe-2009-Zanero-Criscione-Masibty-Web-App-Firewall-slides.pdf +- https://infocon.org/cons/Black%20Hat/Black%20Hat%20USA/Black%20Hat%20USA%202007/presentations/Bolzoni_and_Zambon/Whitepaper/bh-usa-07-bolzoni_and_zambon-WP.pdf +- https://www.blackhat.com/html/bh-media-archives/bh-archives-2007.html#eu_07 + diff --git a/README.rst b/README.rst new file mode 100644 index 0000000..6fb7695 --- /dev/null +++ b/README.rst @@ -0,0 +1,758 @@ +DataRisk Detection Learning Resources +==================================== + +.. image:: https://img.shields.io/github/stars/liaowenzhe/dataRisk-detection-resources.svg + :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers + :alt: GitHub stars + + +.. image:: https://img.shields.io/github/forks/liaowenzhe/dataRisk-detection-resources.svg?color=blue + :target: https://github.com/yzhao062/anomaly-detection-resources/network + :alt: GitHub forks + + +.. image:: https://img.shields.io/github/license/liaowenzhe/dataRisk-detection-resources.svg?color=blue + :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE + :alt: License + + +.. image:: https://awesome.re/badge-flat2.svg + :target: https://awesome.re/badge-flat2.svg + :alt: Awesome + + +---- + +Data security: Based on the "data-centric security system" position, it generally refers to the entire security system focusing on +data classification and the protection of the entire life cycle of sensitive data. + +DataRisk Detection: DataRisk Detection identification is a way to identify possible causes of harm to confidentiality, integrity, availability, etc. of data assets in data processing activities, and to further analyze the threat motive, frequency and possibility of occurrence. + +This repository collects: + +#. Books & Academic Papers +#. Online Courses and Videos +#. DataRisk Datasets +#. Open-source and Commercial Libraries/Toolkits +#. Key Conferences & Journals + + +**More items will be added to the repository**. +Please feel free to suggest other key resources by opening an issue report, +submitting a pull request, or dropping me an email @ (wenzhe.1.liao@gmail.com). +Enjoy reading! + +BTW, you may find my `[GitHub] `_ useful. + +---- + +Table of Contents +----------------- + + +* `1. Books & Tutorials <#1-books--tutorials>`_ + + * `1.1. Books <#11-books>`_ + * `1.2. Tutorials <#12-tutorials>`_ + +* `2. Courses/Seminars/Videos <#2-coursesseminarsvideos>`_ +* `3. Toolbox & Datasets <#3-toolbox--datasets>`_ + + * `3.1. Multivariate data risk detection <#31-multivariate-data>`_ + * `3.2. Time series outlier detection <#32-time-series-outlier-detection>`_ + * `3.3. Real-time Elasticsearch <#33-real-time-elasticsearch>`_ + * `3.4. Datasets <#34-datasets>`_ + +* `4. Papers <#4-papers>`_ + + * `4.1. Overview & Survey Papers <#41-overview--survey-papers>`_ + * `4.2. Key Algorithms <#42-key-algorithms>`_ + * `4.3. Graph & Network Outlier Detection <#43-graph--network-outlier-detection>`_ + * `4.4. Time Series Outlier Detection <#44-time-series-outlier-detection>`_ + * `4.5. Feature Selection in Outlier Detection <#45-feature-selection-in-outlier-detection>`_ + * `4.6. High-dimensional & Subspace Outliers <#46-high-dimensional--subspace-outliers>`_ + * `4.7. Outlier Ensembles <#47-outlier-ensembles>`_ + * `4.8. Outlier Detection in Evolving Data <#48-outlier-detection-in-evolving-data>`_ + * `4.9. Representation Learning in Outlier Detection <#49-representation-learning-in-outlier-detection>`_ + * `4.10. Interpretability <#410-interpretability>`_ + * `4.11. Outlier Detection with Neural Networks <#411-outlier-detection-with-neural-networks>`_ + * `4.12. Active Anomaly Detection <#412-active-anomaly-detection>`_ + * `4.13. Interactive Outlier Detection <#413-interactive-outlier-detection>`_ + * `4.14. Outlier Detection in Other fields <#414-outlier-detection-in-other-fields>`_ + * `4.15. Outlier Detection Applications <#415-outlier-detection-applications>`_ + * `4.16. Automated Outlier Detection <#416-automated-outlier-detection>`_ + * `4.17. Machine Learning Systems for Outlier Detection <#417-machine-learning-systems-for-outlier-detection>`_ + * `4.18. Fairness and Bias in Outlier Detection <#418-fairness-and-bias-in-outlier-detection>`_ + * `4.19. Isolation-based Methods <#419-isolation-based-methods>`_ + * `4.20. Emerging and Interesting Topics <#420-emerging-and-interesting-topics>`_ + +* `5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>`_ + + * `5.1. Conferences & Workshops <#51-conferences--workshops>`_ + * `5.2. Journals <#52-journals>`_ + + +---- + + +1. Books & Tutorials +-------------------- + +1.1. Books +^^^^^^^^^^ + +`Outlier Analysis `_ +by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. +A **must-read** for people in the field of outlier detection. `[Preview.pdf] `_ + +`Outlier Ensembles: An Introduction `_ +by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis. + +`Data Mining: Concepts and Techniques (3rd) `_ +by Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. `[Google Search] `_ + +1.2. Tutorials +^^^^^^^^^^^^^^ + +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== +Tutorial Title Venue Year Ref Materials +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== +Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] `_ +Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] `_ +Anomaly Detection: A Tutorial ICDM 2011 [#Chawla2011Anomaly]_ `[PDF] `_ +Anomaly Detection in Networks KDD 2017 [#Mendiratta2017Anomaly]_ `[Page] `_ +Which Anomaly Detector should I use? ICDM 2018 [#Ting2018Which]_ `[PDF] `_ +Deep Learning for Anomaly Detection KDD 2020 [#Wang2020Deep]_ `[HTML] `_, `[Video] `_ +Deep Learning for Anomaly Detection WSDM 2021 [#Pang2021Deep]_ `[HTML] `_ +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== + +---- + +2. Courses/Seminars/Videos +-------------------------- + +**Coursera Introduction to Anomaly Detection (by IBM)**\ : +`[See Video] `_ + +**Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\ : +`[See Video] `_ + +**Coursera Machine Learning by Andrew Ng also partly covers the topic**\ : + + +* `Anomaly Detection vs. Supervised Learning `_ +* `Developing and Evaluating an Anomaly Detection System `_ + +**Udemy Outlier Detection Algorithms in Data Mining and Data Science**\ : +`[See Video] `_ + +**Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\ : +`[See Video] `_ + +---- + +3. Toolbox & Datasets +--------------------- + +3.1. Multivariate Data +^^^^^^^^^^^^^^^^^^^^^^ + +[**Python**] `Python Outlier Detection (PyOD) `_\ : PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles. + +[**Python**, **GPU**] `TOD: Tensor-based Outlier Detection (PyTOD) `_: A general GPU-accelerated framework for outlier detection. + +[**Python**] `Python Streaming Anomaly Detection (PySAD) `_\ : PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting. + +[**Python**] `Scikit-learn Novelty and Outlier Detection `_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM. + +[**Python**] `Scalable Unsupervised Outlier Detection (SUOD) `_\ : SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD. + +[**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures `_\ : +ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. + +[**Java**] `RapidMiner Anomaly Detection Extension `_\ : The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled. + +[**R**] `CRAN Task View: Anomaly Detection with R `_\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R. + +[**R**] `outliers package `_\ : A collection of some tests commonly used for identifying outliers in R. + +[**Matlab**] `Anomaly Detection Toolbox - Beta `_\ : A collection of popular outlier detection algorithms in Matlab. + + +3.2. Time series outlier detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +[**Python**] `TODS `_\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data. + +[**Python**] `skyline `_\ : Skyline is a near real time anomaly detection system. + +[**Python**] `banpei `_\ : Banpei is a Python package of the anomaly detection. + +[**Python**] `telemanom `_\ : A framework for using LSTMs to detect anomalies in multivariate time series data. + +[**Python**] `DeepADoTS `_\ : A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods. + +[**Python**] `NAB: The Numenta Anomaly Benchmark `_\ : NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. + +[**Python**] `CueObserve `_\ : Anomaly detection on SQL data warehouses and databases. + +[**R**] `CRAN Task View: Anomaly Detection with R `_\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R. + +[**R**] `AnomalyDetection `_\ : AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. + +[**R**] `anomalize `_\ : The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. + +3.3. Real-time Elasticsearch +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +[**Open Distro**] `Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon `_\ : A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See `Real Time Anomaly Detection in Open Distro for Elasticsearch `_. + +[**Python**] `datastream.io `_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana. + + +3.4. Datasets +^^^^^^^^^^^^^ + +**ELKI Outlier Datasets**\ : https://elki-project.github.io/datasets/outlier + +**Outlier Detection DataSets (ODDS)**\ : http://odds.cs.stonybrook.edu/#table1 + +**Unsupervised Anomaly Detection Dataverse**\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF + +**Anomaly Detection Meta-Analysis Benchmarks**\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155 + +**Skoltech Anomaly Benchmark (SKAB)**\ : https://github.com/waico/skab + + +---- + + +4. Papers +--------- + +4.1. Overview & Survey Papers +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Papers are sorted by the publication year. + +====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +A survey of outlier detection methodologies ARTIF INTELL REV 2004 [#Hodge2004A]_ `[PDF] `_ +Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] `_ +A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] `_ +On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ `[HTML] `_, `[SLIDES] `_ +A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ `[PDF] `_ +A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ `[PDF] `_ +Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] `_ +Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] `_ +Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ `[PDF] `_ +Deep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ `[PDF] `_ +Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ `[PDF] `_ +Anomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [#Braei2020Anomaly]_ `[PDF] `_ +Deep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ `[PDF] `_ +A Comprehensive Survey on Graph Anomaly Detection with Deep Learning Preprint 2021 [#Ma2021A]_ `[PDF] `_ +Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] `_, `[Code] `_ +A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges Preprint 2021 [#Salehi2021A]_ `[PDF] `_ +====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + +4.2. Key Algorithms +^^^^^^^^^^^^^^^^^^^ + +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== +Abbreviation Paper Title Venue Year Ref Materials +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== +kNN Efficient algorithms for mining outliers from large data sets ACM SIGMOD Record 2000 [#Ramaswamy2000Efficient]_ `[PDF] `_ +KNN Fast outlier detection in high dimensional spaces PKDD 2002 [#Angiulli2002Fast]_ `[PDF] `_ +LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] `_ +IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] `_ +OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] `_ +AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] `_ +COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] `_ +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== + +4.3. Graph & Network Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== +Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ `[PDF] `_ +Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] `_ +Outlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [#Campos2019Outlier]_ `[PDF] `_ +A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] `_ +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== + + +4.4. Time Series Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] `_ +Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] `_, `[Code] `_ +Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] `_ +Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.5. Feature Selection in Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== +Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings ICDM 2016 [#Pang2016Unsupervised]_ `[PDF] `_ +Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 [#Pang2017Learning]_ `[PDF] `_ +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== + + +4.6. High-dimensional & Subspace Outliers +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= +A survey on unsupervised outlier detection in high-dimensional numerical data Stat Anal Data Min 2012 [#Zimek2012A]_ `[HTML] `_ +Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] `_ +Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [#Radovanovic2015Reverse]_ `[PDF] `_, `[SLIDES] `_ +Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= + + +4.7. Outlier Ensembles +^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] `_ +Ensembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 [#Zimek2014Ensembles]_ `[PDF] `_ +An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [#Campos2018An]_ `[HTML] `_ +LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + +4.8. Outlier Detection in Evolving Data +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction] SIGKDD Explorations 2018 [#Salehi2018A]_ `[PDF] `_ +Unsupervised real-time anomaly detection for streaming data Neurocomputing 2017 [#Ahmad2017Unsupervised]_ `[PDF] `_ +Outlier Detection in Feature-Evolving Data Streams SIGKDD 2018 [#Manzoor2018Outlier]_ `[PDF] `_, `[Github] `_ +Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark ICMLA 2015 [#Lavin2015Evaluating]_ `[PDF] `_, `[Github] `_ +MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams AAAI 2020 [#Bhatia2020MIDAS]_ `[PDF] `_, `[Github] `_ +NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing VLDB 2019 [#Yoon2019NETS]_ `[PDF] `_, `[Github] `_, `[Slide] `_ +Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping KDD 2020 [#Yoon2020STARE]_ `[PDF] `_, `[Github] `_, `[Slide] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.9. Representation Learning in Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] `_ +Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] `_ +XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.10. Interpretability +^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [#Macha2018Explaining]_ `[PDF] `_ +Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] `_ +Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] `_ +Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] `_ +Discriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ `[PDF] `_ +Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] `_ +Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [#Xu2021Beyond]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.11. Outlier Detection with Neural Networks +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] `_, `[Code] `_ +MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks ICANN 2019 [#Li2019MAD]_ `[PDF] `_, `[Code] `_ +Generative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 [#Liu2019Generative]_ `[PDF] `_, `[Code] `_ +Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 [#Zong2018Deep]_ `[PDF] `_, `[Code] `_ +Deep Anomaly Detection with Outlier Exposure ICLR 2019 [#Hendrycks2019Deep]_ `[PDF] `_, `[Code] `_ +Unsupervised Anomaly Detection With LSTM Neural Networks TNNLS 2019 [#Ergen2019Unsupervised]_ `[PDF] `_, `[IEEE] `_, +Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network NeurIPS 2019 [#Wang2019Effective]_ `[PDF] `_ `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.12. Active Anomaly Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Active learning for anomaly and rare-category detection NeurIPS 2005 [#Pelleg2005Active]_ `[PDF] `_ +Outlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] `_ +Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] `_ +Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.13. Interactive Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [#Lamba2019Learning]_ `[PDF] `_ +Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] `_ +eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] `_ +Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.14. Outlier Detection in Other fields +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Field Paper Title Venue Year Ref Materials +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +**Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] `_ +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.15. Outlier Detection Applications +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Field Paper Title Venue Year Ref Materials +======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +**Security** A survey of distance and similarity measures used within network intrusion anomaly detection IEEE Commun. Surv. Tutor. 2015 [#WellerFahy2015A]_ `[PDF] `_ +**Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] `_ +**Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] `_ +**Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] `_ +**Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] `_ +**Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] `_ +**Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] `_ +**Video Surveillance** AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [#Zhou2019AnomalyNet]_ `[IEEE] `_, `Code `_ +======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.16. Automated Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [#Li2020AutoOD]_ `[PDF] `_ +Automatic Unsupervised Outlier Model Selection NeurIPS 2021 [#Zhao2020Automating]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.17. Machine Learning Systems for Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +This section summarizes a list of systems for outlier detection, which may +overlap with the section of tools and libraries. + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +PyOD: A Python Toolbox for Scalable Outlier Detection JMLR 2019 [#Zhao2019PYOD]_ `[PDF] `_, `[Code] `_ +SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection MLSys 2021 [#Zhao2021SUOD]_ `[PDF] `_, `[Code] `_ +TOD: Tensor-based Outlier Detection Preprint 2021 [#Zhao2021TOD]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + + +4.18. Fairness and Bias in Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +A Framework for Determining the Fairness of Outlier Detection ECAI 2020 [#Davidson2020A]_ `[PDF] `_ +FAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + + +4.19. Isolation-Based Methods +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== +Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] `_ +Isolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [#Bandaragoda2018Isolation]_ `[PDF] `_, `[Code] `_ +Extended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] `_, `[Code] `_ +Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [#Ting2020Isolation]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== + + + +4.20. Emerging and Interesting Topics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Clustering with Outlier Removal TKDE 2019 [#Liu2018Clustering]_ `[PDF] `_ +Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning IEEE Trans. Ind. Informat. 2020 [#Castellani2020Siamese]_ `[PDF] `_ +SSD: A Unified Framework for Self-Supervised Outlier Detection ICLR 2021 [#Sehwag2021SSD]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +---- + +5. Key Conferences/Workshops/Journals +------------------------------------- + +5.1. Conferences & Workshops +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Key data mining conference **deadlines**, **historical acceptance rates**, and more +can be found `data-mining-conferences `_. + + +`ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) `_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 `_. + +`ACM International Conference on Management of Data (SIGMOD) `_ + +`The Web Conference (WWW) `_ + +`IEEE International Conference on Data Mining (ICDM) `_ + +`SIAM International Conference on Data Mining (SDM) `_ + +`IEEE International Conference on Data Engineering (ICDE) `_ + +`ACM InternationalConference on Information and Knowledge Management (CIKM) `_ + +`ACM International Conference on Web Search and Data Mining (WSDM) `_ + +`The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) `_ + +`The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) `_ + +5.2. Journals +^^^^^^^^^^^^^ + +`ACM Transactions on Knowledge Discovery from Data (TKDD) `_ + +`IEEE Transactions on Knowledge and Data Engineering (TKDE) `_ + +`ACM SIGKDD Explorations Newsletter `_ + +`Data Mining and Knowledge Discovery `_ + +`Knowledge and Information Systems (KAIS) `_ + +---- + +References +---------- + +.. [#Abe2006Outlier] Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In *Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining*, pp. 504-509, ACM. + +.. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\ , 14(2), pp.49-58. + +.. [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. *Future Generation Computer Systems*\ , 55, pp.278-288. + +.. [#Ahmad2017Unsupervised] Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. 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[#Zhao2019PYOD] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7. + +.. [#Zhao2020Automating] Zhao, Y., Rossi, R.A. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. *Advances in Neural Information Processing Systems*. + +.. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Proceedings of Machine Learning and Systems (MLSys)*. + +.. [#Zhao2021TOD] Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: Tensor-based Outlier Detection. arXiv preprint arXiv:2110.14007. + +.. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*. + +.. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*. + +.. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\ , 5(5), pp.363-387. + +.. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\ , 15(1), pp.11-22. + +.. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR). diff --git a/README_CN.rst b/README_CN.rst new file mode 100644 index 0000000..f846769 --- /dev/null +++ b/README_CN.rst @@ -0,0 +1,553 @@ +异常检测学习资源（Anomaly Detection Learning Resources） +==================================================== + +.. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg + :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers + :alt: GitHub stars + + +.. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue + :target: https://github.com/yzhao062/anomaly-detection-resources/network + :alt: GitHub forks + + +.. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue + :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE + :alt: License + + +.. image:: https://img.shields.io/badge/link-996.icu-red.svg + :target: https://github.com/996icu/996.ICU + :alt: 996.ICU + + +---- + +`异常检测 (anomaly detection) `_ +(又名 Outlier Detection) 是一个重要但非常有挑战性的领域。异常检测的目标主要是找到数据中 +偏离于主要分布的案例--它在很多领域都有重要意义，包括「信用卡诈骗检测」、「网络入侵检测」、 +「机械故障检测」等。 + +这个仓库中收藏了关于异常检测的： + + +#. 专业书籍与学术论文 +#. 在线课程与视频 +#. 异常检测数据集 +#. 开源与商业工具库 +#. 重要的会议与期刊 + + +**更多内容会被陆续添加到当前仓库中来**。 +请建议/推荐相关资源，你可以选择提交issue report、pull request或者给我发邮件 (zhaoy@cmu.edu)。 +Enjoy reading！ + +---- + +目录 +----------------- + + +* `1. 书籍 & 教程 <#1-书籍--教程>`_ + + * `1.1. 书籍 <#11-书籍>`_ + * `1.2. 教程 <#12-教程>`_ + +* `2. Courses/Seminars/Videos <#2-coursesseminarsvideos>`_ +* `3. Toolbox & Datasets <#3-toolbox--datasets>`_ + + * `3.1. Multivariate data outlier detection <#31-multivariate-data>`_ + * `3.2. Time series outlier detection <#32-time-series-outlier-detection>`_ + * `3.3. Datasets <#33-datasets>`_ + +* `4. Papers <#4-papers>`_ + + * `4.1. Overview & Survey Papers <#41-overview--survey-papers>`_ + * `4.2. Key Algorithms <#42-key-algorithms>`_ + * `4.3. Graph & Network Outlier Detection <#43-graph--network-outlier-detection>`_ + * `4.4. Time Series Outlier Detection <#44-time-series-outlier-detection>`_ + * `4.5. Feature Selection in Outlier Detection <#45-feature-selection-in-outlier-detection>`_ + * `4.6. High-dimensional & Subspace Outliers <#46-high-dimensional--subspace-outliers>`_ + * `4.7. Outlier Ensembles <#47-outlier-ensembles>`_ + * `4.8. Outlier Detection in Evolving Data <#48-outlier-detection-in-evolving-data>`_ + * `4.9. Representation Learning in Outlier Detection <#49-representation-learning-in-outlier-detection>`_ + * `4.10. Interpretability <#410-interpretability>`_ + * `4.11. Outlier Detection with Neural Networks <#411-outlier-detection-with-neural-networks>`_ + * `4.12. Active Anomaly Detection <#412-active-anomaly-detection>`_ + * `4.13. Interactive Outlier Detection <#413-interactive-outlier-detection>`_ + * `4.14. Outlier Detection in Other fields <#414-outlier-detection-in-other-fields>`_ + * `4.15. Outlier Detection Applications <#415-outlier-detection-applications>`_ + +* `5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>`_ + + * `5.1. Conferences & Workshops <#51-conferences--workshops>`_ + * `5.2. Journals <#52-journals>`_ + + +---- + +1. 书籍 & 教程 +------------- + +1.1. 书籍 +^^^^^^^^ + +`Outlier Analysis `_ +作者: Charu Aggarwal: 经典异常检测教科书，内容涵盖了大部分相关算法与应用。异常检测领域人士必读。 +`[预览.pdf] `_ + +`Outlier Ensembles: An Introduction `_ +作者: Charu Aggarwal and Saket Sathe: 非常权威的集成异常检测教科书。 + +`Data Mining: Concepts and Techniques (3rd) `_ +作者: 韩家炜 (Jiawei Han) and Micheline Kamber and Jian Pei (裴健): 该书第十二章讨论了异常检测技术。 `[Google Search] `_ + +1.2. 教程 +^^^^^^^^ + +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== +Tutorial Title Venue Year Ref Materials +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== +Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] `_ +Anomaly Detection: A Tutorial ICDM 2011 [#Chawla2011Anomaly]_ `[PDF] `_ +Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] `_ +===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== + +---- + +2. Courses/Seminars/Videos +-------------------------- + +**Coursera Introduction to Anomaly Detection (by IBM)**\ : +`[See Video] `_ + +**Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\ : +`[See Video] `_ + +**Coursera Machine Learning by Andrew Ng also partly covers the topic**\ : + + +* `Anomaly Detection vs. Supervised Learning `_ +* `Developing and Evaluating an Anomaly Detection System `_ + +**Udemy Outlier Detection Algorithms in Data Mining and Data Science**\ : +`[See Video] `_ + +**Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\ : +`[See Video] `_ + +---- + +3. Toolbox & Datasets +--------------------- + +3.1. Multivariate Data +^^^^^^^^^^^^^^^^^^^^^^ + +[**Python**] `Python Outlier Detection (PyOD) `_\ : PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles. + +[**Python**] `Scikit-learn Novelty and Outlier Detection `_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM. + +[**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures `_\ : +ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. + +[**Java**] `RapidMiner Anomaly Detection Extension `_\ : The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled. + +[**R**] `outliers package `_\ : A collection of some tests commonly used for identifying outliers in R. + +[**Matlab**] `Anomaly Detection Toolbox - Beta `_\ : A collection of popular outlier detection algorithms in Matlab. + + +3.2. Time series outlier detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + +[**Python**] `datastream.io `_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana. + +[**Python**] `skyline `_\ : Skyline is a near real time anomaly detection system. + +[**Python**] `banpei `_\ : Banpei is a Python package of the anomaly detection. + +[**Python**] `telemanom `_\ : A framework for using LSTMs to detect anomalies in multivariate time series data. + +[**Python**] `DeepADoTS `_\ : A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods. + +[**R**] `AnomalyDetection `_\ : AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. + + +3.3. Datasets +^^^^^^^^^^^^^ + +**ELKI Outlier Datasets**\ : https://elki-project.github.io/datasets/outlier + +**Outlier Detection DataSets (ODDS)**\ : http://odds.cs.stonybrook.edu/#table1 + +**Unsupervised Anomaly Detection Dataverse**\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF + +**Anomaly Detection Meta-Analysis Benchmarks**\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155 + +---- + +4. Papers +--------- + +4.1. Overview & Survey Papers +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +A survey of outlier detection methodologies ARTIF INTELL REV 2004 [#Hodge2004A]_ `[PDF] `_ +Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] `_ +A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] `_ +On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ `[HTML] `_, `[SLIDES] `_ +A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ `[PDF] `_ +A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ `[PDF] `_ +Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] `_ +Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + +4.2. Key Algorithms +^^^^^^^^^^^^^^^^^^^ + +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== +Abbreviation Paper Title Venue Year Ref Materials +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== +kNN Efficient algorithms for mining outliers from large data sets ACM SIGMOD Record 2000 [#Ramaswamy2000Efficient]_ `[PDF] `_ +KNN Fast outlier detection in high dimensional spaces PKDD 2002 [#Angiulli2002Fast]_ `[PDF] `_ +LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] `_ +IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] `_ +OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] `_ +AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] `_ +==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== + +4.3. Graph & Network Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== +Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ `[PDF] `_ +Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] `_ +================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== + + +4.4. Time Series Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] `_ +Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.5. Feature Selection in Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== +Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings ICDM 2016 [#Pang2016Unsupervised]_ `[PDF] `_ +Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 [#Pang2017Learning]_ `[PDF] `_ +================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== + + +4.6. High-dimensional & Subspace Outliers +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= +A survey on unsupervised outlier detection in high-dimensional numerical data Stat Anal Data Min 2012 [#Zimek2012A]_ `[HTML] `_ +Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] `_ +Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [#Radovanovic2015Reverse]_ `[PDF] `_, `[SLIDES] `_ +Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= + + +4.7. Outlier Ensembles +^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] `_ +Ensembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 [#Zimek2014Ensembles]_ `[PDF] `_ +An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [#Campos2018An]_ `[HTML] `_ +LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + +4.8. Outlier Detection in Evolving Data +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction] SIGKDD Explorations 2018 [#Salehi2018A]_ `[PDF] `_ +Unsupervised real-time anomaly detection for streaming data Neurocomputing 2017 [#Ahmad2017Unsupervised]_ `[PDF] `_ +Outlier Detection in Feature-Evolving Data Streams SIGKDD 2018 [#Manzoor2018Outlier]_ `[PDF] `_, `[Github] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.9. Representation Learning in Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] `_ +Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] `_ +XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.10. Interpretability +^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [#Macha2018Explaining]_ `[PDF] `_ +Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] `_ +Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] `_ +Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] `_ +Discriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ `[PDF] `_ +Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.11. Outlier Detection with Neural Networks +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] `_, `[Code] `_ +MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks Preprint 2019 [#Li2019MAD]_ `[PDF] `_, `[Code] `_ +Generative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 [#Liu2019Generative]_ `[PDF] `_, `[Code] `_ +Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 [#Zong2018Deep]_ `[PDF] `_, `[Code] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.12. Active Anomaly Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== +Active learning for anomaly and rare-category detection NeurIPS 2005 [#Pelleg2005Active]_ `[PDF] `_ +Outlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] `_ +Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] `_ +================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== + + +4.13. Interactive Outlier Detection +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Paper Title Venue Year Ref Materials +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [#Lamba2019Learning]_ `[PDF] `_ +Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] `_ +eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] `_ +Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] `_ +================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.14. Outlier Detection in Other fields +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Field Paper Title Venue Year Ref Materials +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +**Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] `_ +============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +4.15. Outlier Detection Applications +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +=================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +Field Paper Title Venue Year Ref Materials +=================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== +**Security** A survey of distance and similarity measures used within network intrusion anomaly detection IEEE Commun. Surv. Tutor. 2015 [#WellerFahy2015A]_ `[PDF] `_ +**Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] `_ +**Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] `_ +**Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] `_ +**Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] `_ +**Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] `_ +**Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] `_ +=================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== + + +---- + +5. Key Conferences/Workshops/Journals +------------------------------------- + +5.1. Conferences & Workshops +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Key data mining conference **deadlines**, **historical acceptance rates**, and more +can be found `data-mining-conferences `_. + + +`ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) `_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2018 `_. + +`ACM International Conference on Management of Data (SIGMOD) `_ + +`The Web Conference (WWW) `_ + +`IEEE International Conference on Data Mining (ICDM) `_ + +`SIAM International Conference on Data Mining (SDM) `_ + +`IEEE International Conference on Data Engineering (ICDE) `_ + +`ACM InternationalConference on Information and Knowledge Management (CIKM) `_ + +`ACM International Conference on Web Search and Data Mining (WSDM) `_ + +`The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) `_ + +`The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) `_ + +5.2. Journals +^^^^^^^^^^^^^ + +`ACM Transactions on Knowledge Discovery from Data (TKDD) `_ + +`IEEE Transactions on Knowledge and Data Engineering (TKDE) `_ + +`ACM SIGKDD Explorations Newsletter `_ + +`Data Mining and Knowledge Discovery `_ + +`Knowledge and Information Systems (KAIS) `_ + +---- + +References +---------- + +.. [#Abe2006Outlier] Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In *Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining*, pp. 504-509, ACM. + +.. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\ , 14(2), pp.49-58. + +.. [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. *Future Generation Computer Systems*\ , 55, pp.278-288. + +.. [#Ahmad2017Unsupervised] Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. Unsupervised real-time anomaly detection for streaming data. *Neurocomputing*, 262, pp.134-147. + +.. [#Akoglu2015Graph] Akoglu, L., Tong, H. and Koutra, D., 2015. Graph based anomaly detection and description: a survey. *Data Mining and Knowledge Discovery*\ , 29(3), pp.626-688. + +.. [#Angiulli2002Fast] Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In *European Conference on Principles of Data Mining and Knowledge Discovery*, pp. 15-27. + +.. [#Arnaldo2019ex2] Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In *ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA)*. + +.. [#Breunig2000LOF] Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. *ACM SIGMOD Record*\ , 29(2), pp. 93-104. + +.. [#Campos2016On] Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I. and Houle, M.E., 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. *Data Mining and Knowledge Discovery*\ , 30(4), pp.891-927. + +.. [#Campos2018An] Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In *Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576)*. Springer, Cham. + +.. [#Chandola2009Anomaly] Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. *ACM computing surveys* , 41(3), p.15. + +.. [#Chawla2011Anomaly] Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. *Tutorial at ICDM 2011*. + +.. [#Chen2017Outlier] Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. *SIAM International Conference on Data Mining*, pp. 90-98. 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International Conference on Learning Representations (ICLR). diff --git a/download.py b/download.py new file mode 100644 index 0000000..c1617ee --- /dev/null +++ b/download.py @@ -0,0 +1,32 @@ +#!/usr/bin/python + +""" + This script will download all papers/books and rename to proper name + if there is no copyright issue. + + TODO: download resources by item number + TODO: add exception handler for downloader +""" +import re +import pathlib +import urllib.request + +# initialize the log directory if it does not exist +pathlib.Path('resources').mkdir(parents=True, exist_ok=True) + +f = open('resource_urls\\papers.txt', 'r') +for line in f: + # print(line) + line_splits = line.split(' | ') + + # remove all special char in file name + file_name = re.sub(r'[\\/*?:"<>|]', "", line_splits[0]) + # strip filename length in case it is too long + if len(file_name) > 255: + file_name = file_name[:255] + url = line_splits[1] + + print('Downloading', file_name, 'from', url) + urllib.request.urlretrieve(url, "resources\\" + file_name + '.pdf') + +f.close() diff --git a/resource_urls/1 b/resource_urls/1 new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/resource_urls/1 @@ -0,0 +1 @@ + diff --git a/resource_urls/papers.txt b/resource_urls/papers.txt new file mode 100644 index 0000000..9036ab1 --- /dev/null +++ b/resource_urls/papers.txt @@ -0,0 +1,6 @@ +Anomaly detection: A survey | https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf +A survey of outlier detection methodologies | https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf +A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data | http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable +Outlier detection for temporal data: A survey | https://pdfs.semanticscholar.org/18d1/714870fb989f32b4311892e8765f00f7098f.pdf +Ensembles for unsupervised outlier detection: challenges and research questions a position paper | http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf +Outlier ensembles: position paper | https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf \ No newline at end of file diff --git a/url_checker.py b/url_checker.py new file mode 100644 index 0000000..0945222 --- /dev/null +++ b/url_checker.py @@ -0,0 +1,47 @@ +import re +import requests + + +def exists(path): + """ Utility function to check whether a web file exists + :param path: + :return: + """ + r = requests.head(path) + # print(r.status_code) + return r.status_code == requests.codes.ok + + +def exists_adv(path): + """ Utility function to check whether a web file exists + :param path: + :return: + """ + # TODO: use selenium + r = requests.head(path) + # print(r.status_code) + return r.status_code == requests.codes.ok + + +if __name__ == "__main__": + # driver = webdriver.Firefox() + manual_links = [] + potential_broken_links = [] + with open('README_11232019.md', encoding="utf-8") as f: + lines = f.readlines() + + for line in lines: + result = re.search(']\(http(.*)\)', line) + if result: + link = "http" + result.group(1) + if "github" in link or "coursera" in link: + manual_links.append(link) + else: + flag = exists(link) + if not flag: + potential_broken_links.append(link) + print(link) + + print() + for link in manual_links: + print(link)