吉林大学学报（理学版）2025，Vol.63Issue(6)：1731-1736,6.DOI:10.13413/j.cnki.jdxblxb.2024304
基于多模态深度神经网络的Web网页攻击重定向混淆检测
Web Page Attack Redirection Confusion Detection Based on Multimodal Deep Neural Network
摘要
Abstract
Aiming at the problem that malicious Web page links and plugins could be attached to other files through constant confusion and deformation,traditional detection methods were difficult to achieve accurate detection,we proposed a Web page attack redirection confusion detection method based on multimodal deep neural networks.Firstly,we extracted the features of Web page attacks:attribute class,keyword class,var class,and word class,and converted them into 8-dimensional sensitive feature vectors to calculate their corresponding real values.Secondly,the Web page and real values were input together into a multimodal deep neural network for training.Finally,accurate attack redirection confusion detection results were obtained through the output of the Web page classifier.The experimental results show that the detection rate of the proposed method is about 98％,which can effectively detect redirection confusion in Web page attacks while ensuring a high detection rate.关键词
多模态深度神经网络/Web网页攻击重定向混淆检测/TF-IDF算法/非线性激励单元/损失函数Key words
multimodal deep neural network/Web page attack redirection confusion detection/TF-IDF algorithm/nonlinear excitation unit/loss function分类
信息技术与安全科学引用本文复制引用
闫培玲,刘俊娟,高志宇..基于多模态深度神经网络的Web网页攻击重定向混淆检测[J].吉林大学学报（理学版）,2025,63(6):1731-1736,6.基金项目
全国高等院校计算机基础教育研究会计算机基础教育教学研究项目(批准号:2023-AFCEC-243)、河南省高等学校重点科研项目(批准号:24A630021)和教育部产学合作协同育人项目(批准号:231100440174038). （批准号:2023-AFCEC-243）
