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HMM XSS检测

转自:http://www.freebuf.com/articles/web/133909.html

前言

上篇我们介绍了HMM的基本原理以及常见的基于参数的异常检测实现,这次我们换个思路,把机器当一个刚入行的白帽子,我们训练他学会XSS的攻击语法,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。

原理图.png

通过词法分割,可以把攻击载荷序列化成观察序列,举例如下:

序列化.jpg

词集/词袋模型

词集和词袋模型是机器学习中非常常用的一个数据处理模型,它们用于特征化字符串型数据。一般思路是将样本分词后,统计每个词的频率,即词频,根据需要选择全部或者部分词作为哈希表键值,并依次对该哈希表编号,这样就可以使用该哈希表对字符串进行编码。

  • 词集模型:单词构成的集合,集合自然每个元素都只有一个,也即词集中的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,并统计其出现的次数

本章使用词集模型即可。

假设存在如下数据集合:

    dataset = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],          ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],          ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],          ['stop', 'posting', 'stupid', 'worthless', 'garbage'],          ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],          ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]

首先生成词汇表:

vocabSet = set()
for doc in dataset:
vocabSet |= set(doc)
vocabList = list(vocabSet)

根据词汇表生成词集:

# 词集模型

SOW = []
for doc in dataset:
vec = [0]*len(vocabList)
for i, word in enumerate(vocabList):
if word in doc:
vec[i] = 1
SOW.append(doc)

 

简化后的词集模型的核心代码如下:

fredist = nltk.FreqDist(tokens_list) # 单文件词频
keys=fredist.keys()
keys=keys[:max] #只提取前N个频发使用的单词 其余泛化成0
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集中
continue
else:
wordbag[localkey] = index_wordbag
index_wordbag += 1

数据处理与特征提取

常见的XSS攻击载荷列举如下:

<script>alert('XSS')</script>
%3cscript%3ealert('XSS')%3c/script%3e
%22%3e%3cscript%3ealert('XSS')%3c/script%3e
<IMG SRC="javascript:alert('XSS');">
<IMG SRC=javascript:alert("XSS")>
<IMG SRC=javascript:alert('XSS')>
<img src=xss onerror=alert(1)>
<IMG """><SCRIPT>alert("XSS")</SCRIPT>">
<IMG SRC=javascript:alert(String.fromCharCode(88,83,83))>
<IMG SRC="jav ascript:alert('XSS');">
<IMG SRC="jav ascript:alert('XSS');">
<BODY BACKGROUND="javascript:alert('XSS')">
<BODY ONLOAD=alert('XSS')>

需要支持的词法切分原则为:

单双引号包含的内容 ‘XSS’

http/https链接 http://xi.baidu.com/xss.js

<>标签 <script>

<>标签开头 <BODY

属性标签 ONLOAD=

<>标签结尾 >

函数体 “javascript:alert(‘XSS’);”

字符数字标量 代码实现举例如下:

tokens_pattern = r'''(?x)
"[^"]+"
|http://\S+
|</\w+>
|<\w+>
|<\w+
|\w+=
|>
|\w+\([^<]+\) #函数 比如alert(String.fromCharCode(88,83,83))
|\w+
'''
words=nltk.regexp_tokenize(line, tokens_pattern)

另外,为了减少向量空间,需要把数字和字符以及超链接范化,具体原则为:

#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)
范化后分词效果示例为:
#原始参数值:"><img src=x onerror=prompt(0)>)
#分词后:
['>', '<img', 'src=', 'x', 'onerror=', 'prompt(8)', '>']

 

#原始参数值:<iframe src="x-javascript:alert(document.domain);"></iframe>)
#分词后:
['<iframe', 'src=', '"x-javascript:alert(document.domain);"', '>', '</iframe>']
#原始参数值:<marquee><h1>XSS by xss</h1></marquee> )
#分词后:
['<marquee>', '<h8>', 'XSS', 'by', 'xss', '</h8>', '</marquee>']
#原始参数值:<script>-=alert;-(1)</script> "onmouseover="confirm(document.domain);"" </script>)
#分词后:
['<script>', 'alert', '8', '</script>', '"onmouseover="', 'confirm(document.domain)', '</script>']
#原始参数值:<script>alert(2)</script> "><img src=x onerror=prompt(document.domain)>)
#分词后:
['<script>', 'alert(8)', '</script>', '>', '<img', 'src=', 'x', 'onerror=', 'prompt(document.domain)', '>']

结合词集模型,完整的流程举例如下:

词集模型处理流程.png

训练模型

将范化后的向量X以及对应的长度矩阵X_lens输入即可,需要X_lens的原因是参数样本的长度可能不一致,所以需要单独输入。

remodel = hmm.GaussianHMM(n_components=3, covariance_type="full", n_iter=100)
remodel.fit(X,X_lens)

验证模型

整个系统运行过程如下:

系统运行流程.png

验证阶段利用训练出来的HMM模型,输入观察序列获取概率,从而判断观察序列的合法性,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,全部样本就用这200个特征进行编码并序列化,使用20000条正常日志和20000条XSS攻击识别(类似JSFUCK这类编码的暂时不支持),准确率达到90%以上,其中验证环节的核心代码如下:

with open(filename) as f:
for line in f:
line = line.strip('\n')
line = urllib.unquote(line)
h = HTMLParser.HTMLParser()
line = h.unescape(line)
if len(line) >= MIN_LEN:
line, number = re.subn(r'\d+', "8", line)
line, number = re.subn(r'(http|https)://[a-zA-Z0-9\.@&/#!#\?:]+', "http://u", line)
line, number = re.subn(r'\/\*.?\*\/', "", line)
words = do_str(line)
vers = []
for word in words:
if word in wordbag.keys():
vers.append([wordbag[word]])
else:
vers.append([-1])
np_vers = np.array(vers)
pro = remodel.score(np_vers)
if pro >= T:
print "SCORE:(%d) XSS_URL:(%s) " % (pro,line)

较完整的代码如下:

# -*- coding:utf-8 -*-

import sys
import urllib
import urlparse
import re
from hmmlearn import hmm
import numpy as np
from sklearn.externals import joblib
import HTMLParser
import nltk


#处理参数值的最小长度
MIN_LEN=10

#状态个数
N=5
#最大似然概率阈值
T=-200
#字母
#数字 1
#<>,:"'
#其他字符2
SEN=['<','>',',',':','\'','/',';','"','{','}','(',')']

index_wordbag=1 #词袋索引
wordbag={} #词袋

#</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 ischeck(str):
    if re.match(r'^(http)',str):
        return False
    for i, c in enumerate(str):
        if ord(c) > 127 or ord(c) < 31:
            return False
        if c in SEN:
            return True
        #排除中文干扰 只处理127以内的字符


    return False


def do_str(line):
    words=nltk.regexp_tokenize(line, tokens_pattern)
    #print  words
    return words

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=urllib.unquote(line)
            #处理html转义字符
            h = HTMLParser.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=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
def main(filename):
    X = [[-1]]
    X_lens = [1]
    X = []
    X_lens = []
    global wordbag
    global index_wordbag

    with open(filename) as f:
        for line in f:
            line=line.strip('\n')
            #url解码
            line=urllib.unquote(line)
            #处理html转义字符
            h = HTMLParser.HTMLParser()
            line=h.unescape(line)
            vers=[]
            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)
                for word in words:
                    if word in wordbag.keys():
                        vers.append([wordbag[word]])
                    else:
                        vers.append([-1])
                    print word, vers
            np_vers = np.array(vers)
            print "np_vers:", np_vers, "X:", X
            #print np_vers
            X=np.concatenate([X,np_vers])
            X_lens.append(len(np_vers))
            #print X_lens



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

    return remodel

def test(remodel,filename):
    with open(filename) as f:
        for line in f:
            line = line.strip('\n')
            # url解码
            line = urllib.unquote(line)
            # 处理html转义字符
            h = HTMLParser.HTMLParser()
            line = h.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)
                #print np_vers
                        #print  "CHK SCORE:(%d) QUREY_PARAM:(%s) XSS_URL:(%s) " % (pro, v, line)
                pro = remodel.score(np_vers)

                if pro >= T:
                    print  "SCORE:(%d) XSS_URL:(%s) " % (pro,line)
                        #print line

def test_normal(remodel,filename):
    with open(filename) as f:
        for line in f:
            # 切割参数
            result = urlparse.urlparse(line)
            # url解码
            query = urllib.unquote(result.query)
            params = urlparse.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 np_vers
                    # print  "CHK SCORE:(%d) QUREY_PARAM:(%s) XSS_URL:(%s) " % (pro, v, line)
                    pro = remodel.score(np_vers)
                    print  "CHK SCORE:(%d) QUREY_PARAM:(%s)" % (pro, v)
                    #if pro >= T:
                        #print  "SCORE:(%d) XSS_URL:(%s) " % (pro, v)
                        #print line

if __name__ == '__main__':
    #test(remodel,sys.argv[2])
    load_wordbag(sys.argv[1],2000)
    #print  wordbag.keys()
    remodel = main(sys.argv[1])
    #test_normal(remodel, sys.argv[2])
    test(remodel, sys.argv[2])

 

posted on 2017-12-04 19:56 bonelee 阅读(...) 评论(...) 编辑 收藏