Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (2): 118-128.doi: 10.6040/j.issn.1672-3961.0.2019.043

• Machine Learning & Data Mining • Previous Articles    

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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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

CLC Number: 

  • TP309.2

Fig.1

Schematic diagram of intelligence detection of oriented to big data Cross-Site Scripting attack with semantic scenario analysis and vectorization"

Table 1

Recognition rates for the firstⅠclass based on different μ"

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

Fig.2

Curve diagram of recognition rates for the first Ⅰ class based on different μ"

Table 2

Recognition rates for the second Ⅱ class based on different μ"

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

Table 3

Recognition rates for the firstⅠclass based on different BatchSize"

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

Table 4

Recognition rates for the secondⅡclass based on different BatchSize"

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

Table 5

Recognition rates for the firstⅠclass based on different number of neurons"

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

Table 6

Recognition rates for the secondⅡclass based on different number of neurons"

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

Table 7

Average recognition rates, variance and standard deviation for the firstⅠclass based on different μ %"

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

Table 8

Average recognition rates, variance and standard deviation for the secondⅡclass based on different μ %"

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

Table 9

Average recognition rates, variance and standard deviation for the firstⅠclass based on different BatchSize %"

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

Table 10

Average recognition rates, variance and standard deviation for the secondⅡ class based ondifferent BatchSize %"

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

Table 11

Average recognition rates, variance and standard deviation for the firstⅠclass based on different number of neurons %"

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

Table 12

Average recognition rates, variance and standard deviation for the secondⅡclass based on different number of neurons %"

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

Fig.3

Bar chart of average recognition rate for the firstⅠand secondⅡ class based on different μ"

Fig.4

Bar chart of standard deviation for the firstⅠand second Ⅱ class based on different μ"

Fig.5

Curve diagram of recognition rate change"

Fig.6

Curve diagram of error of loss change"

Fig.7

Curve diagram of cosine proximity change"

Fig.8

Curve diagram of mean absolute error change"

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