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ML_for_SQL/README

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+本项目是使用机器学习算法来分类SQL注入语句与正常语句：
+使用了SVM,Adaboost，决策树，随机森林，逻辑斯蒂回归，KNN，贝叶斯等算法分别对SQL注入语句与正常语句进行分类。
+data是收集的样本数据
+file中存放的是训练好的各个模型
+featurepossess.py是对原始样本进行预处理，提特征。
+sqlsvm.py等py文件是训练模型
+testsql是对训练好的模型进行测试，用准确率来度量模型效果。

ML_for_SQL/adaboost.py

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+# -*- 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
+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
+
+