基于类图像处理与向量化的大数据脚本攻击智能检测  

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

作　　者：张海军[1] 陈映辉[2] ZHANG Haijun;CHEN Yinghui(College of Computer Science,Jiaying University,Meizhou,Guangdong 514015,China;School of Mathematics,Jiaying University,Meizhou,Guangdong 514015,China)

机构地区：[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

出　　处：《计算机工程》2020年第3期129-137,143,共10页Computer Engineering

基　　金：国家自然科学基金(61171141,61573145);广东省自然科学基金重点项目(2014B010104001,2015A030308018);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课题(18KYKT11);广东省嘉应学院自然科学基金重点项目(2017KJZ02)。

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]