融合DRAE与SVM的网页防篡改检测  

Fusing DRAE and SVM for webpage tamper-resistant detection

作　　者：邢金阁[1] 张鑫 周长建[1] XING Jin’ge;ZHANG Xin;ZHOU Changjian(Center of Modern Education Technology,Northeast Agricultural University,Harbin,Heilongjiang 150030,China;School of Electrical and Information,Northeast Agricultural University,Harbin,Heilongjiang 150030,China)

机构地区：[1]东北农业大学现代教育技术中心,黑龙江哈尔滨150030 [2]东北农业大学电气与信息学院,黑龙江哈尔滨150030

出　　处：《福州大学学报（自然科学版）》2023年第5期652-656,共5页Journal of Fuzhou University(Natural Science Edition)

基　　金：黑龙江省高等教育教学改革研究资助项目(SJGY20220178);中国教育技术协会高等农业院校分会2023年度智慧校园专项课题资助项目(C23ZD02)。

摘　　要：针对传统的网络安全研究,如入侵检测、流量分析和主动防御等方法需要较强网络安全相关知识、大量网络训练数据,以及较高研究门槛的问题,本研究提出一种基于深度残差自动编码器(deep residual auto-encoder,DRAE)与支持向量机(SVM)相结合的网页防篡改检测模型,运用DRAE提取网页图像特征,并输入SVM分类器以判别网页是否被篡改.在某大学范围内实验验证,结果表明,使用该模型进行网页检测的准确率高达95%,高于现有检测方法.Traditional cyber security research methods such as intrusion detection,traffic analysis,and active defense require strong network security related knowledge,a large amount of network training data,and a high research threshold.This study proposes a webpage tamper-proof detection model based on deep residual auto-encoder(DRAE)and support vector machine(SVM).The model uses DRAE to extract webpage image features and input SVM classifier to determine whether the webpage is tampered with.After experimental verification in a certain university,the results show that the accuracy of web page detection using this model is as high as 95%,which is higher than the existing detection methods.

关 键 词：网页防篡改 深度残差自动编码器 支持向量机 网络安全 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]