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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (2): 118-128.doi: 10.6040/j.issn.1672-3961.0.2019.043

• 机器学习与数据挖掘 • 上一篇    

语义分析及向量化大数据跨站脚本攻击智检

张海军1(),陈映辉2,*()   

  1. 1. 嘉应学院计算机学院, 广东 梅州 514015
    2. 嘉应学院数学学院, 广东 梅州 514015
  • 收稿日期:2019-01-29 出版日期:2020-04-20 发布日期:2020-04-16
  • 通讯作者: 陈映辉 E-mail:nihaoba_456@163.com
  • 作者简介:张海军(1978—)，男，江西赣州人，讲师，博士，主要研究方向为智能计算，智能控制，模式识别，深度学习等. E-mail：nihaoba_456@163.com
  • 基金资助:
    国家自然科学基金资助项目(61171141);国家自然科学基金资助项目(61573145);广东省自然科学基金重点资助项目(2014B010104001);广东省自然科学基金重点资助项目(2015A030308018);广东省普通高等学校人文社会科学省市共建重点研究基地资助项目(18KYKT11);广东省嘉应学院自然科学基金重点资助项目(2017KJZ02)

Semantic analysis and vectorization for intelligent detection of big data cross-site scripting attacks

Haijun ZHANG1(),Yinghui CHEN2,*()   

  1. 1. School of Computer, Jiaying University, Meizhou 514015, Guangdong, China
    2. School of Mathematics, Jiaying University, Meizhou 514015, Guangdong, China
  • Received:2019-01-29 Online:2020-04-20 Published:2020-04-16
  • Contact: Yinghui CHEN E-mail:nihaoba_456@163.com
  • Supported by:
    国家自然科学基金资助项目(61171141);国家自然科学基金资助项目(61573145);广东省自然科学基金重点资助项目(2014B010104001);广东省自然科学基金重点资助项目(2015A030308018);广东省普通高等学校人文社会科学省市共建重点研究基地资助项目(18KYKT11);广东省嘉应学院自然科学基金重点资助项目(2017KJZ02)

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摘要：

基于语义情景分析及向量化对访问流量语料库大数据进行词向量化处理,实现面向大数据跨站脚本攻击智能检测。利用自然语言处理方法进行数据获取、数据清洗、数据抽样、特征提取等数据预处理。设计基于神经网络的词向量化算法,实现词向量化得到词向量大数据;通过理论分析和推导,实现多种不同深度的长短时记忆网络智能检测算法。设计不同的超参数并进行反复试验,分别得到最大识别率为0.999 5、最低识别率为0.264 3、识别率均值为99.88%、方差为0、标准差为0.000 4的识别率变化过程曲线图、损失误差变化过程曲线图、词向量样本余弦距离变化曲线图和平均绝对误差变化过程曲线图等。研究结果表明该算法有高识别率、稳定性强、总体性能优良等优点。

关键词: 网络入侵检测, 跨站脚本攻击, 自然语言处理, 深度长短时记忆网络, 大数据

Abstract:

The access traffic corpus big data were processed with word vectorization based on the methods of semantic scenario analysis and vectorization, and the intelligent detection oriented to big data cross-site scripting attack was realized. It used the natural language processing methods for data acquisition, data cleaning, data sampling, feature extraction and other data preprocessing. The algorithm of word vectorization based on neural network was used to realize word vectorization and get big data of word vectorization. Through theoretical analysis and deductions, the intelligent detection algorithms of varieties of long short term memory networks with different layers were realized. With different hyperparameters and repeated tests, lots of results were got, such as the highest recognition rate for 0.999 5, the minimum recognition rate for 0.264 3, average recognition rate for 99.88%, variance for 0, standard deviations for 0.000 4, the curve diagram of recognition rates change, the curve diagram of error of loss change, the curve diagram of cosine proximity change of word vector samples and the curve diagram of mean absolute error change etc. The results of the study showed that the algorithm had the advantages of high recognition rates, strong stability and excellent overall performance, etc.

Key words: web intrusion detection, cross-site scripting, natural language processing, deep long short term memory network, big data

中图分类号: 

  • TP309.2

图1

基于语义情景分析及向量化面向大数据跨站脚本攻击智能检测原理图"

表1

第Ⅰ类基于不同的μ的识别率"

序号 学习率
0.001 0.01 0.1
1 0.988 5 0.982 9 0.278 4
2 0.994 1 0.994 4 0.273 7
3 0.994 3 0.994 8 0.278 7
4 0.994 8 0.995 3 0.264 3
5 0.995 2 0.995 5 0.280 1
6 0.995 5 0.995 7 0.279 7
7 0.995 7 0.995 6 0.278 9
8 0.995 9 0.995 8 0.281 1
9 0.996 0 0.995 8 0.279 6
10 0.996 1 0.996 1 0.278 3
11 0.996 3 0.996 1 0.278 7
12 0.996 4 0.996 0 0.282 5
13 0.996 5 0.995 7 0.279 1
14 0.996 5 0.996 0 0.279 2
15 0.996 7 0.996 1 0.275 1
16 0.996 6 0.996 4 0.279 0
17 0.995 7 0.996 5 0.279 0
18 0.996 3 0.996 5 0.279 6
19 0.996 5 0.996 6 0.270 7
20 0.996 7 0.996 8 0.280 4

图2

第Ⅰ类基于不同的μ的识别率曲线图"

表2

第Ⅱ类基于不同的μ的识别率"

序号 学习率
0.001 0.01 0.1
1 0.995 2 0.993 8 0.956 8
2 0.997 4 0.997 5 0.969 4
3 0.997 9 0.997 6 0.927 5
4 0.998 3 0.998 1 0.830 9
5 0.998 6 0.998 4 0.831 0
6 0.998 8 0.998 4 0.831 0
7 0.998 9 0.998 8 0.831 1
8 0.999 1 0.999 0 0.830 9
9 0.999 2 0.999 0 0.831 1
10 0.999 2 0.999 0 0.831 1
11 0.999 3 0.999 1 0.830 8
12 0.999 3 0.999 2 0.831 1
13 0.999 3 0.999 3 0.831 3
14 0.999 3 0.999 3 0.831 1
15 0.999 5 0.999 3 0.831 2
16 0.999 5 0.999 4 0.831 0
17 0.999 4 0.999 5 0.830 9
18 0.999 5 0.999 3 0.831 2
19 0.999 4 0.999 4 0.831 1
20 0.999 5 0.999 5 0.831 0

表3

第Ⅰ类基于不同的BatchSize的识别率"

序号 数据块
50 100 500
1 0.993 2 0.993 5 0.982 9
2 0.993 4 0.9942 0.994 4
3 0.993 9 0.994 6 0.994 8
4 0.994 2 0.994 8 0.995 3
5 0.993 8 0.994 9 0.995 5
6 0.994 5 0.994 6 0.995 6
7 0.994 5 0.994 6 0.995 6
8 0.994 5 0.993 3 0.995 8
9 0.994 5 0.994 2 0.995 8
10 0.994 7 0.994 6 0.996 1
11 0.994 4 0.993 4 0.996 1
12 0.994 6 0.994 9 0.996 0
13 0.994 6 0.994 9 0.995 7
14 0.994 5 0.995 2 0.996 0
15 0.994 7 0.995 3 0.996 1
16 0.994 7 0.995 1 0.996 4
17 0.994 9 0.995 2 0.996 5
18 0.994 9 0.995 3 0.996 5
19 0.994 9 0.994 9 0.996 6
20 0.995 2 0.994 7 0.996 8

表4

第Ⅱ类基于不同的BatchSize的识别率"

序号 数据块
50 100 500
1 0.994 9 0.993 8 0.992 9
2 0.997 5 0.997 5 0.996 9
3 0.997 9 0.997 6 0.997 3
4 0.998 3 0.998 1 0.997 6
5 0.998 7 0.998 4 0.998 0
6 0.998 9 0.998 4 0.998 3
7 0.999 0 0.998 8 0.998 4
8 0.999 1 0.999 0 0.998 5
9 0.999 1 0.999 0 0.998 8
10 0.999 1 0.999 0 0.998 8
11 0.999 2 0.999 1 0.999 0
12 0.999 2 0.999 2 0.999 1
13 0.999 3 0.999 3 0.999 0
14 0.999 4 0.999 3 0.999 2
15 0.999 3 0.999 3 0.999 3
16 0.999 4 0.999 4 0.999 1
17 0.999 4 0.999 5 0.999 3
18 0.999 4 0.999 3 0.999 3
19 0.999 3 0.999 4 0.999 3
20 0.999 4 0.999 5 0.999 3

表5

第Ⅰ类基于不同的神经元数的识别率"

序号 神经元数
64 128 256
1 0.993 2 0.991 1 0.991 8
2 0.993 4 0.993 6 0.993 5
3 0.993 9 0.993 9 0.993 5
4 0.994 2 0.992 6 0.993 2
5 0.993 8 0.993 9 0.993 4
6 0.994 5 0.994 0 0.992 7
7 0.994 5 0.994 1 0.993 5
8 0.994 5 0.994 5 0.993 8
9 0.993 8 0.994 1 0.993 9
10 0.994 7 0.994 2 0.994 0
11 0.994 4 0.994 3 0.994 1
12 0.994 6 0.994 3 0.994 3
13 0.994 6 0.993 8 0.991 6
14 0.994 5 0.991 4 0.993 4
15 0.994 7 0.994 1 0.993 8
16 0.994 7 0.994 2 0.993 8
17 0.994 9 0.994 4 0.993 5
18 0.994 9 0.994 5 0.992 3
19 0.994 9 0.994 5 0.993 7
20 0.995 2 0.994 7 0.993 3

表6

第Ⅱ类基于不同的神经元数的识别率"

序号 神经元数
64 128 256
1 0.993 8 0.996 8 0.993 9
2 0.997 5 0.996 6 0.994 0
3 0.997 5 0.996 6 0.994 0
4 0.998 1 0.997 2 0.995 4
5 0.998 4 0.997 1 0.994 0
6 0.998 4 0.996 8 0.994 8
7 0.998 8 0.996 8 0.994 9
8 0.999 0 0.996 8 0.994 9
9 0.999 0 0.995 0 0.995 0
10 0.999 0 0.996 0 0.995 0
11 0.999 1 0.995 0 0.994 9
12 0.999 2 0.995 0 0.994 8
13 0.999 3 0.997 1 0.994 8
14 0.999 3 0.996 7 0.994 8
15 0.999 3 0.994 3 0.994 8
16 0.999 4 0.996 6 0.994 7
17 0.999 5 0.996 6 0.994 8
18 0.999 3 0.996 9 0.994 8
19 0.999 4 0.997 0 0.994 8
20 0.999 5 0.997 0 0.995 0

表7

第Ⅰ类基于不同的μ的平均识别率、方差和标准差"

学习率 平均识别率 方差 标准差
0.1 99.551 5 0.000 3 0.182 1
1 99.523 0 0.000 8 0.296 1
10 27.780 5 0.001 6 0.411 6

表8

第Ⅱ类基于不同的μ的平均识别率、方差和标准差"

学习率 平均识别率 方差 标准差
0.1 99.883 0 0.000 1 0.102 9
1 99.864 5 0.000 2 0.128 8
10 84.907 5 0.188 8 4.457 9

表9

第Ⅰ类基于不同的BatchSize的平均识别率、方差和标准差"

数据块 平均识别率 方差 标准差
5 000 99.443 0 0 0.050 6
10 000 99.461 0 0 0.060 8
50 000 99.522 5 0.000 8 0.296 0

表10

第Ⅱ类基于不同的BatchSize的平均识别率、方差和标准差"

数据块 平均识别率 方差 标准差
5 000 99.879 0 0.000 1 0.105 5
10 000 99.864 5 0.000 2 0.128 8
20 000 99.837 0 0.000 2 0.147 3

表11

第Ⅰ类基于不同的神经元数的平均识别率、方差和标准差"

神经元数 平均识别率 方差 标准差
6 400 99.443 0 0 0.050 6
12 800 99.381 0 0.000 1 0.098 2
25 600 99.335 5 0.000 1 0.072 7

表12

第Ⅱ类基于不同的神经元数的平均识别率、方差和标准差"

神经元数 平均识别率 方差 标准差
6 400 99.864 5 0.000 2 0.128 8
12 800 99.640 0 0.000 1 0.085 8
25 600 99.470 5 0.000 0 0.040 2

图3

第Ⅰ和Ⅱ类基于不同μ的识别率均值条形图"

图4

第Ⅰ和Ⅱ类基于不同μ的标准差条形图"

图5

识别率变化曲线图"

图6

损失误差变化曲线图"

图7

余弦距离变化曲线图"

图8

平均绝对误差变化曲线图"

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