《Web安全之机器学习入门》笔记：第九章 9.3 支持向量机算法SVM 检测XSS攻击

        本小节是通过网上搜集的数据使用svm算法识别XSS攻击。

一、支持向量机

        支持向量机SVM（support vector machines）是一种二分类模型，它的目的是寻找一个超平面来对样本进行分割，分割的原则是间隔最大化，最终转化为一个凸二次规划问题来求解。

二、数据集

        构造黑白样本，黑样本20w，白样本20w

etl('../data/xss-200000.txt',x,1)
etl('../data/good-xss-200000.txt',x,0)

三、向量特征化

        对于数据集文件，每项提取出四个特征，分别是：url长度、url中包含的第三方域名的个数、敏感字符的个数、敏感关键字的个数。

        如下为源码，注意相对于书里配套代码，增加了encoding='utf-8'，否则会报错

def get_len(url):
    return len(url)

def get_url_count(url):
    if re.search('(http://)|(https://)', url, re.IGNORECASE) :
        return 1
    else:
        return 0

def get_evil_char(url):
    return len(re.findall("[<>,\'\"/]", url, re.IGNORECASE))

def get_evil_word(url):
    return len(re.findall("(alert)|(script=)(%3c)|(%3e)|(%20)|(onerror)|(onload)|(eval)|(src=)|(prompt)",url,re.IGNORECASE))

def etl(filename,data,isxss):
        with open(filename, encoding='utf-8') as f:
            for line in f:
                f1=get_len(line)
                f2=get_url_count(line)
                f3=get_evil_char(line)
                f4=get_evil_word(line)
                data.append([f1,f2,f3,f4])
                if isxss:
                    y.append(1)
                else:
                    y.append(0)
        return data

打印数据集X的前四个数据

[23, 0, 2, 0] [106, 0, 7, 5] [16, 0, 2, 0] [24, 0, 2, 0] 

四、训练数据

如下所示，训练与测试集的比例为6：4

x_train, x_test, y_train, y_test = model_selection.train_test_split(x,y, test_size=0.4, random_state=0)
clf = svm.SVC(kernel='linear', C=1).fit(x_train, y_train)
y_pred = clf.predict(x_test)

五、完整源码

import re
import joblib
from sklearn import model_selection
from sklearn import svm
from sklearn import metrics

x = []
y = []


def get_len(url):
    return len(url)

def get_url_count(url):
    if re.search('(http://)|(https://)', url, re.IGNORECASE) :
        return 1
    else:
        return 0

def get_evil_char(url):
    return len(re.findall("[<>,\'\"/]", url, re.IGNORECASE))

def get_evil_word(url):
    return len(re.findall("(alert)|(script=)(%3c)|(%3e)|(%20)|(onerror)|(onload)|(eval)|(src=)|(prompt)",url,re.IGNORECASE))

def do_metrics(y_test,y_pred):
    print("metrics.accuracy_score:")
    print(metrics.accuracy_score(y_test, y_pred))
    print("metrics.confusion_matrix:")
    print(metrics.confusion_matrix(y_test, y_pred))
    print("metrics.precision_score:")
    print(metrics.precision_score(y_test, y_pred))
    print("metrics.recall_score:")
    print(metrics.recall_score(y_test, y_pred))
    print("metrics.f1_score:")
    print(metrics.f1_score(y_test, y_pred))

def etl(filename,data,isxss):
        with open(filename, encoding='utf-8') as f:
            for line in f:
                f1=get_len(line)
                f2=get_url_count(line)
                f3=get_evil_char(line)
                f4=get_evil_word(line)
                data.append([f1,f2,f3,f4])
                if isxss:
                    y.append(1)
                else:
                    y.append(0)
        return data

etl('../data/xss-200000.txt',x,1)
etl('../data/good-xss-200000.txt',x,0)

x_train, x_test, y_train, y_test = model_selection.train_test_split(x,y, test_size=0.4, random_state=0)
clf = svm.SVC(kernel='linear', C=1).fit(x_train, y_train)
y_pred = clf.predict(x_test)

do_metrics(y_test, y_pred)

joblib.dump(clf,"xss-svm-200000-module.m")

六、运行结果

metrics.accuracy_score:
0.9979229856257418
metrics.confusion_matrix:
[[54092    73]
 [   53  6446]]
metrics.precision_score:
0.988801963491333
metrics.recall_score:
0.9918448992152639
metrics.f1_score:
0.9903210938700261

七、模型验证

clf1 = joblib.load("xss-svm-200000-module.m")
y_test1 = []
y_test1 = clf1.predict(x)
print(metrics.accuracy_score(y_test1, y))

运行结果

0.9979493333685002