《Web安全之机器学习入门》笔记:第十二章 12.4 隐式马尔可夫算法识别XSS攻击(二)

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本小节示例另一种马尔可夫识别XSS攻击的方法。

本小节通过将机器当做小白,训练它学会XSS攻击语法,然后再让机器从访问日志中寻找符合攻击语法的疑似攻击日志。

img

整个系统运行流程如下所示:

img

词集模型:单词构成的集合,集合中自然每个元素都只有一个,即词集中每个单词都只有一个 词袋模型:如果一个单词在文档中出现不止一次,统计出现的次数 

通常是先生存词汇表,再生成词集。训练样本是1000条典型的XSS攻击日志,通过分词、计算词集,提炼出200个特征,全部样本就使用这200个特征进行编码并序列化。

img为了减少向量空间,需要把数字、字符以及超链接泛化,代码如下: 

#数字常量替换成8
line, number = re.subn(r'\d+', "8", line)
#url 换成http://u
line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:=]+', "http://u", line)
#干掉注释
line, number = re.subn(r'\/\*.?\*\/', "", line)

分词则是需要基于XSS特征,代码如下:

#</script><script>alert(String.fromCharCode(88,83,83))</script>
#<IMG SRC=x onchange="alert(String.fromCharCode(88,83,83))">
#<;IFRAME SRC=http://ha.ckers.org/scriptlet.html <;
#';alert(String.fromCharCode(88,83,83))//\';alert(String.fromCharCode(88,83,83))//";alert(String.fromCharCode(88,83,83))
# //\";alert(String.fromCharCode(88,83,83))//--></SCRIPT>">'><SCRIPT>alert(String.fromCharCode(88,83,83))</SCRIPT>
tokens_pattern = r'''(?x)
 "[^"]+"
|http://\S+
|</\w+>
|<\w+>
|<\w+
|\w+=
|>
|\w+\([^<]+\) #函数 比如alert(String.fromCharCode(88,83,83))
|\w+
'''
def do_str(line):
    words=nltk.regexp_tokenize(line, tokens_pattern)
    #print(words)
    return words

数据集处理部分代码逻辑如下:

def load_wordbag(filename,max=100):
    X = [[0]]
    X_lens = [1]
    tokens_list=[]
    global wordbag
    global index_wordbag

    with open(filename) as f:
        for line in f:
            line=line.strip('\n')
            #url解码
            line=parse.unquote(line)
            #处理html转义字符
            h = HTMLParser()
            line=h.unescape(line)
            if len(line) >= MIN_LEN:
                #print ("Learning xss query param:(%s)" % line)
                #数字常量替换成8
                line, number = re.subn(r'\d+', "8", line)
                #ulr日换成http://u
                line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:=]+', "http://u", line)
                #干掉注释
                line, number = re.subn(r'\/\*.?\*\/', "", line)
                #print ("Learning xss query etl param:(%s) " % line)
                tokens_list+=do_str(line)

            #X=np.concatenate( [X,vers])
            #X_lens.append(len(vers))

    fredist = nltk.FreqDist(tokens_list)  # 单文件词频
    keys=list(fredist.keys())
    keys=keys[:max]
    for localkey in keys:  # 获取统计后的不重复词集
        if localkey in wordbag.keys():  # 判断该词是否已在词袋中
            continue
        else:
            wordbag[localkey] = index_wordbag
            index_wordbag += 1

    print("GET wordbag size(%d)" % index_wordbag)

(1)模型保存

训练好的模型保存为xss-train.pkl,代码如下

remodel = hmm.GaussianHMM(n_components=N, covariance_type="full", n_iter=100)
    remodel.fit(X,X_lens)
    joblib.dump(remodel, "xss-train.pkl")

(2)模型训练

训练样本为xss黑样本,训练时的数据集处理的完整代码如下:

def train(filename):
    X = [[-1]]
    X_lens = [1]
    global wordbag
    global index_wordbag

    with open(filename, encoding='utf-8') as f:
        for line in f:
            line=line.strip('\n')
            #url解码
            line=parse.unquote(line)
            #处理html转义字符
            line=html.unescape(line)
            if len(line) >= MIN_LEN:
                #print ("Learning xss query param:(%s)" % line)
                #数字常量替换成8
                line, number = re.subn(r'\d+', "8", line)
                #ulr日换成http://u
                line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", line)
                #干掉注释
                line, number = re.subn(r'\/\*.?\*\/', "", line)
                #print ("Learning xss query etl param:(%s) " % line)
                words=do_str(line)
                vers=[]
                for word in words:
                    #print ("ADD %s" % word)
                    if word in wordbag.keys():
                        vers.append([wordbag[word]])
                    else:
                        vers.append([-1])

            np_vers = np.array(vers)
            X=np.concatenate([X,np_vers])
            X_lens.append(len(np_vers))

    print(len(X_lens), X_lens)
    remodel = hmm.GaussianHMM(n_components=N, covariance_type="full", n_iter=100)
    remodel.fit(X,X_lens)
    joblib.dump(remodel, "xss-train.pkl")

    return remodel

(3)优化训练 

这里我认为代码应该使用基于黑样本特征而训练,即

def train_black(filename, train_model):
    X = [[-1]]
    X_lens = [1]
    global wordbag
    global index_wordbag

    with open(filename, encoding='utf-8') as f:
        for line in f:
            # 切割参数
            result = parse.urlparse(line)
            # url解码
            query = parse.unquote(result.query)
            params = parse.parse_qsl(query, True)
            for k, v in params:
                v=v.strip('\n')
                #print  "CHECK v:%s LINE:%s " % (v, line)

                if len(v) >= MIN_LEN:
                    # print  "CHK XSS_URL:(%s) " % (line)
                    # 数字常量替换成8
                    v, number = re.subn(r'\d+', "8", v)
                    # ulr日换成http://u
                    v, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", v)
                    # 干掉注释
                    v, number = re.subn(r'\/\*.?\*\/', "", v)
                    # print "Learning xss query etl param:(%s) " % line
                    words = do_str(v)
                    # print "GET Tokens (%s)" % words
                    vers = []
                    for word in words:
                        # print "ADD %s" % word
                        if word in wordbag.keys():
                            vers.append([wordbag[word]])
                        else:
                            vers.append([-1])

                    np_vers = np.array(vers)
                    X = np.concatenate([X, np_vers])
                    X_lens.append(len(np_vers))

    print(len(X_lens), X_lens)
    remodel = hmm.GaussianHMM(n_components=3, covariance_type="full", n_iter=100)
    remodel.fit(X,X_lens)
    joblib.dump(remodel, train_model)

    return remodel

(1)测试模型

 如下代码逻辑是基于白样本的测试函数,具体如下

def test(filename):
    remodel = joblib.load("xss-train.pkl")
    i = 1
    x = []
    y = []
    with open(filename, encoding='utf-8') as f:
        for line in f:
            #print(i,line)
            i= i +1
            line = line.strip('\n')
            # url解码
            line = parse.unquote(line)
            # 处理html转义字符
            line = html.unescape(line)

            if len(line) >= MIN_LEN:
                #print("CHK XSS_URL:(%s) " % (line))
                # 数字常量替换成8
                line, number = re.subn(r'\d+', "8", line)
                # ulr日换成http://u
                line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", line)
                # 干掉注释
                line, number = re.subn(r'\/\*.?\*\/', "", line)
                #print("Learning xss query etl param:(%s) " % line)
                words = do_str(line)
                #print("GET Tokens (%s)" % words)
                vers = []
                for word in words:
                    # print(("ADD %s" % word)
                    if word in wordbag.keys():
                        vers.append([wordbag[word]])
                    else:
                        vers.append([-1])

                np_vers = np.array(vers)
                pro = remodel.score(np_vers)
                x.append(len(vers))
                y.append(pro)

    return x, y

基于上一小节的理解,可以将其分为基于白样本和黑样本两类:

 (2)黑样本优化测试

def test_black(filename, train_model):
    remodel=joblib.load(train_model)
    x = []
    y = []
    with open(filename, encoding='utf-8') as f:
        for line in f:
            # 切割参数
            result = parse.urlparse(line)
            # url解码
            query = parse.unquote(result.query)
            params = parse.parse_qsl(query, True)

            for k, v in params:
                v=v.strip('\n')
                #print  "CHECK v:%s LINE:%s " % (v, line)

                if len(v) >= MIN_LEN:
                    # print  "CHK XSS_URL:(%s) " % (line)
                    # 数字常量替换成8
                    v, number = re.subn(r'\d+', "8", v)
                    # ulr日换成http://u
                    v, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", v)
                    # 干掉注释
                    v, number = re.subn(r'\/\*.?\*\/', "", v)
                    # print "Learning xss query etl param:(%s) " % line
                    words = do_str(v)
                    # print "GET Tokens (%s)" % words
                    vers = []
                    for word in words:
                        # print "ADD %s" % word
                        if word in wordbag.keys():
                            vers.append([wordbag[word]])
                        else:
                            vers.append([-1])

                    np_vers = np.array(vers)
                    #print("CHK SCORE:(%d) QUREY_PARAM:(%s) XSS_URL:(%s) " % (pro, v, line))
                    pro = remodel.score(np_vers)
                    x.append(len(vers))
                    y.append(pro)


    return x, y

(3)白样本优化测试

def test_white(filename, train_model):
    remodel = joblib.load(train_model)
    i = 1
    x = []
    y = []
    with open(filename, encoding='utf-8') as f:
        for line in f:
            #print(i,line)
            i= i +1
            line = line.strip('\n')
            # url解码
            line = parse.unquote(line)
            # 处理html转义字符
            line = html.unescape(line)

            if len(line) >= MIN_LEN:
                #print("CHK XSS_URL:(%s) " % (line))
                # 数字常量替换成8
                line, number = re.subn(r'\d+', "8", line)
                # ulr日换成http://u
                line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", line)
                # 干掉注释
                line, number = re.subn(r'\/\*.?\*\/', "", line)
                #print("Learning xss query etl param:(%s) " % line)
                words = do_str(line)
                #print("GET Tokens (%s)" % words)
                vers = []
                for word in words:
                    # print("ADD %s" % word)
                    if word in wordbag.keys():
                        vers.append([wordbag[word]])
                    else:
                        vers.append([-1])

                np_vers = np.array(vers)
                pro = remodel.score(np_vers)
                x.append(len(vers))
                y.append(pro)

    return x, y

使用正常日志与XSS攻击日志进行测试。

pro = remodel.score(np_vers)

完整代码 

if __name__ == '__main__':
    train_file = 'xss-10000.txt'
    load_wordbag(train_file,200)
    remodel = main(train_file)

    x1, y1 = test('./good-xss-10000.txt')
    x2, y2 = test('./xss-10000.txt')

    fig,ax=plt.subplots()
    ax.set_xlabel('Line Length')
    ax.set_ylabel('HMM Score')
    ax.scatter(x2, y2, color='g', label="xss",marker='v')
    ax.scatter(x1, y1, color='r', label="good",marker='*')

    ax.legend(loc='best')
    plt.show()

 y1和y2的长度如下

8018 10000

图示如下 

img

 (1)黑样本训练

if __name__ == '__main__':
    train_file = 'xss-10000.txt'
    train_model = '"xss-train_black.pkl"'
    load_wordbag(train_file,200)
    remodel = train_black(train_file, train_model)

 (2)黑样本测试

这里使用黑样本训练,用黑白样本分别测试:

if __name__ == '__main__':
    train_file = 'xss-10000.txt'
    train_model = '"xss-train_black.pkl"'
    load_wordbag(train_file,200)
    remodel = train_black(train_file, train_model)
    x1, y1 = test_black('./good-xss-10000.txt',train_model)
    x2, y2 = test_black('./xss-10000.txt', train_model)
    print(len(y1), len(y2))
    fig,ax=plt.subplots()
    ax.set_xlabel('Line Length')
    ax.set_ylabel('HMM Score')
    ax.scatter(x2, y2, color='g', label="xss",marker='v')
    ax.scatter(x1, y1, color='r', label="good",marker='*')

    ax.legend(loc='best')
    plt.show()

 这里测试模型过滤后白样本仅193,黑样本为16072个

193 16072

img

 (3)白样本函数

if __name__ == '__main__':
    train_file = 'xss-10000.txt'
    train_model = '"xss-train_black.pkl"'
    load_wordbag(train_file,200)
    remodel = train_black(train_file, train_model)
    x1, y1 = test_write('./good-xss-10000.txt',train_model)
    x2, y2 = test_write('./xss-10000.txt', train_model)
    print(len(y1), len(y2))
    fig,ax=plt.subplots()
    ax.set_xlabel('Line Length')
    ax.set_ylabel('HMM Score')
    ax.scatter(x2, y2, color='g', label="xss",marker='v')
    ax.scatter(x1, y1, color='r', label="good",marker='*')

    ax.legend(loc='best')
    plt.show()

测试结果

8018 10000

图示如下

img

(4)黑样本用黑样本函数,白样本用白函数混用

黑样本用黑样本函数,白样本用白样本函数

if __name__ == '__main__':
    train_file = 'xss-10000.txt'
    train_model = '"xss-train_black.pkl"'
    load_wordbag(train_file,200)
    remodel = train_black(train_file, train_model)
    x1, y1 = test_white('./good-xss-10000.txt',train_model)
    x2, y2 = test_black('./xss-10000.txt', train_model)
    print(len(y1), len(y2))
    fig,ax=plt.subplots()
    ax.set_xlabel('Line Length')
    ax.set_ylabel('HMM Score')
    ax.scatter(x2, y2, color='g', label="xss",marker='v')
    ax.scatter(x1, y1, color='r', label="good",marker='*')

    ax.legend(loc='best')
    plt.show()

 测试结果

8018 16072

图示如下

img

         不过黑样本训练后,使用三种测试效果都不咋地,也就特征过于明显的test_write效果稍微好一点点。

        总体来讲,这两小节的实验效果除了test_write这种方式的分词方式,无论是白生成黑,还是黑生成白,哪种训练和测试方法效果都不太明显。而且我发现词汇长度相似的情况下,两小节的score效果也相近。

        因为作者配套源码未有给出如何运行,未给出明确数据集,书上给的参数与源码很多都不一致,也没有详细注释,修改后多次调试训练结果不好,此节只是暂时初步理解这种方法即可。

        后续如有深刻理解,会更新此笔记,也欢迎指教。

本文为互联网自动采集或经作者授权后发布,本文观点不代表立场,若侵权下架请联系我们删帖处理!文章出自:https://blog.csdn.net/mooyuan/article/details/122771041
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