Intelligent Detection for Big Data Scripting Attack Based on Image Processing Inspired Method and Vectorization

doi:10.19678/j.issn.1000-3428.0053360

Abstract

Abstract: In this paper,the methods similar to image processing and vectorization are used for the verctorization of access traffic corpus big data,and the intelligent detection for big data cross-site scripting attack is achieved.Besides,this paper uses methods similar to image processing for data acquisition,data cleaning,data sampling and feature extraction.Then,a word vectorization algorithm based on neural network is designed to obtain the big data of word vectorization.On this basis,the DCNNs intelligent detection algorithm with different depth is proposed.Finally,experiments with different hyper-parameter are conducted,and the obtained average recognition rate,variance and standard deviation show that the proposed algorithm has high recognition rate and stability.

Key words: Web intrusion detection, Cross-Site Scripting(XSS) attack, natural language processing, big data, cyberspace security

摘要： 通过类图像处理与向量化方法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的智能检测。利用类图像处理方法进行数据获取、数据清洗、数据抽样和特征提取，设计一种基于神经网络的词向量化算法，得到词向量化大数据。在此基础上，提出多种不同深度的DCNNs智能检测算法。设置不同的超参数进行实验得到算法的识别率均值、方差和标准差，结果表明，该算法具有较高的识别率和稳定性。

关键词: Web入侵检测, 跨站脚本攻击, 自然语言处理, 大数据, 网络空间安全

CLC Number:

TP391

ZHANG Haijun, CHEN Yinghui. Intelligent Detection for Big Data Scripting Attack Based on Image Processing Inspired Method and Vectorization[J]. Computer Engineering, 2020, 46(3): 129-137,143.

张海军, 陈映辉. 基于类图像处理与向量化的大数据脚本攻击智能检测[J]. 计算机工程, 2020, 46(3): 129-137,143.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0053360

http://www.ecice06.com/EN/Y2020/V46/I3/129

Figures/Tables 28

References

[1] NAIR A.Prevention of cross site scripting and securing Web application atclient side[EB/OL].[2018-11-20].http://www.ijaerd.com/papers/special_papers/RTDE021.pdf.
[2] GERMÁN E R,BENAVIDES D E,TORRES J,et al.Cookie scout:an analytic model for prevention of cross-site scripting using a cookie classifier[C]//Proceedings of International Conference on Information Theoretic Security.Berlin,Germany:Springer,2018:497-507.
[3] WANG Guihua,QIN Xiangqing,CHEN Li,et al.A query recommendation algorithm for professional search engines[J].Computer Engineering and Applications,2013,49(9):144-149.(in Chinese) 王桂华,秦湘清,陈黎,等.一种面向专业搜索引擎的查询推荐算法[J].计算机工程与应用,2013,49(9):144-149.
[4] ZHANG Haijun,ZHANG Nan,XIAO Nanfeng.Fire detection and identification method based on visual attention mechanism[J].Optik,2015,126:5011-5018.
[5] LIU Quan,LIANG Bin,XU Jin,et al.A deep hierarchical neural network model for aspect-based sentiment analysis[J].Chinese Journal of Computers,2018,41(12):2637-2652.(in Chinese) 刘全,梁斌,徐进,等.一种用于基于方面情感分析的深度分层网络模型[J].计算机学报,2018,41(12):2637-2652.
[6] REHMAN Y A U,MAN P L,LIU M.LiveNet:improving features generalization for face liveness detection using convolution neural networks[J].Expert Systems with Applications,2018,108:159-169.
[7] ZHU Z A,LI Y Z,SONG Z.On the convergence rate of training recurrent neural networks[EB/OL].[2018-11-20].https://www.microsoft.com/en-us/research/uploads/prod/2018/12/on-the-convergence-rate.pdf.
[8] DU S S,ZHAI X,POCZOS B,et al.Gradient descent provably optimizes over-parameterized neural networks[EB/OL].[2018-11-20].https://arxiv.org/abs/1810.02054?context=cs.LG.
[9] LEVINE S,PASTOR P,KRIZHEVSKY A,et al.Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J].Inter-national Journal of Robotics Research,2016(10):421-436.
[10] WANG Lei,ZHOU Qing,HE Dongjie,et al.Multi-source taint analysis technique for privacy leak detection of Android Apps[J].Journal of Software,2019,30(2):211-230.(in Chinese)王蕾,周卿,何冬杰,等.面向Android应用隐私泄露检测的多源污点分析技术[J].软件学报,2019,30(2):211-230.
[11] GAO Yanjun,ZHANG Xueying,LI Fenglian,et al.Data allocation algorithm for large data with all-to-all comparison based on graph covering[J].Computer Engineering,2018,44(4):17-22,27.(in Chinese)高燕军,张雪英,李凤莲,等.基于图覆盖的大数据全比较数据分配算法[J].计算机工程,2018,44(4):17-22,27.
[12] KWON O,LEE N,SHIN B.Data quality management,data usage experience and acquisition intention of big data analytics[J].International Journal of Information Management,2014,34(3):387-394.
[13] CHEN Q,SOKOLOVA M.Word2Vec and Doc2Vec in unsupervised sentiment analysis of clinical discharge summaries[EB/OL].[2018-11-20].https://arxiv.org/ftp/arxiv/papers/1805/1805.00352.pdf.
[14] Deep learning for Java[EB/OL].[2018-11-20].https://deeplearning4j.org/.
[15] CELESTI F,CELESTI A,WAN J,et al.Why deep learning is changing the way to approach NGS data processing:a review[J].IEEE Reviews in Biomedical Engineering,2018,11:68-76.
[16] SURBHI G,ADAM K,RAGHU M.Learning one convolutional layer with overlapping patches[EB/OL].[2018-11-20].http://proceedings.mlr.press/v80/goel18a/goel18a.pdf.
[17] YU Yanwei,JIA Zhaofei,CAO Lei,et al.Fast density-based clustering algorithm for location big data[J].Journal of Software,2018,29(8):2470-2484.(in Chinese)于彦伟,贾召飞,曹磊,等.面向位置大数据的快速密度聚类算法[J].软件学报,2018,29(8):2470-2484.
[18] ZHANG Haijun,XIAO Nanfeng.Parallel implementation of multilayered neural networks based on map-reduce on cloud computing clusters[J].Soft Computing,2016,20(4):1471-1483.
[19] TRIGUERO I,PERALTA D,BACARDIT J,et al.MRPR:a MapReduce solution for prototype reduction in big data classification[J].Neurocomputing,2015,150:331-345.
[20] DU S S,LEE J D,TIAN Y,et al.Gradient descent learns one-hidden-layer CNN:don't be afraid of spurious local minima[EB/OL].[2018-11-20].https://arxiv.org/pdf/1712.00779.pdf.
[21] JAEGUL C,SHIXIA L.Visual analytics for explainable deep learning[J].IEEE Computer Graphics and Applications,2018,38(4):84-92.
[22] MICHAËL G,CHEN J,BARRON J T,et al.Deep bilateral learning for real-time image enhancement[J].ACM Transactions on Graphics,2017,36(4):118-120.

Please choose a citation manager

Content to export