Webpage mining attack detection method and system based on deep learning and multi-dimensional feature recognition

Through deep learning-based methods, web mining traffic detection model and JavaScript script detection model are trained, and the original data is directly input, which solves the shortcomings of detecting web mining attacks in the existing technology, and achieves higher generalization capabilities and detection accuracy.

CN119696803AActive Publication Date: 2025-03-25THE THIRD RES INST OF MIN OF PUBLIC SECURITY +1
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Patent Information

Application Number
CN202311238683.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2023-09-25
Publication Date
2025-03-25
Estimated Expiration
2043-09-25

AI Technical Summary

Technical Problem

The existing technology is difficult to effectively detect web mining attacks, and there are technical problems such as insufficient check rate, low check rate, and inability to deal with code obfuscation and concealment, as well as poor feature extraction bias and generalization ability caused by human factors.

Method used

Using deep learning and multi-dimensional feature recognition methods, by training the web mining traffic detection model based on VGG-16 and the web mining JavaScript script detection model based on BiLSTM, the original traffic data and JavaScript scripts are directly used as inputs to realize end-to-end web mining attack detection without manually designing features.

Benefits of technology

It improves the generalization and robustness of detection methods, reduces false alarms and misreports caused by JavaScript code obfuscation, and enhances the detection ability of mining JavaScript scripts. It can detect more mining websites while maintaining low false alarms and misreports, with high accuracy and recall.

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Abstract

The invention discloses a webpage mining attack detection method and system based on deep learning and multi-dimensional feature recognition, and the method comprises the steps: carrying out the preprocessing of collected flow at an offline stage, generating a gray-scale map, and respectively training two webpage mining flow detection models based on VGG-16; after the extracted JavaScript is subjected to anti-confusion and character replacement processing, the JavaScript is respectively used for training two webpage mining JavaScript detection models based on BiLSTM (Bidirectional Long Short Term Memory); and in the online stage, classification is carried out through the trained webpage mining flow detection model and the webpage mining JavaScript script detection model, and classification results are subjected to multi-dimensional feature fusion, so that webpage mining attack detection is realized. According to the method, mining behaviors are detected from two aspects of mining network flow and mining JavaScript. According to the method, the original flow data and the JavaScript are directly used as input, end-to-end webpage mining attack detection is achieved, manual feature design is not needed, and therefore the generalization ability and robustness of the detection method are improved.
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