A mobile webpage camouflage detection method and system
By simulating webpage access on desktop and mobile devices, extracting features and using the XGBoost model, the problem of mobile webpage spoofing detection in existing technologies is solved, achieving efficient and accurate mobile webpage spoofing recognition and classification.
CN119166918BActive Publication Date: 2026-06-12TSINGHUA UNIVERSITY
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2024-07-30
- Publication Date
- 2026-06-12
AI Technical Summary
⚠Technical Problem
Existing methods for detecting website spoofing mainly focus on the differences between desktop and mobile content, lacking effective detection of mobile website spoofing, which increases the difficulty of detection.
⚗Method used
By identifying search keywords for mobile webpage spoofing, and using simulated desktop and mobile webpage access, textual, visual, network, element, and URL features are extracted. The XGBoost model is then used for classification to achieve accurate identification of mobile webpage spoofing.
🎯Benefits of technology
It achieves efficient and accurate mobile webpage spoofing detection, can instantly identify and classify normal webpages and mobile webpage spoofing, reduces false alarm rate, and has good scalability and coverage.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN119166918B_ABST
Abstract
The application provides a mobile webpage camouflage detection method and system, which comprises the following steps: determining the search keywords and search results of mobile webpage camouflage; accessing the webpage on a preset simulated desktop terminal and a preset simulated mobile terminal respectively according to the search results to obtain webpage access data; the webpage access data comprises training data and test data; extracting features from the test data to obtain classification features; the classification features are used to distinguish normal webpages and mobile webpage camouflage; the types of feature extraction include text features, visual features, network features, element features and URL features; inputting the classification features into a pre-trained classification model to obtain the mobile webpage camouflage detection results output by the classification model; and the classification model is trained based on the training data. The application can effectively identify and classify normal webpages and mobile webpage camouflage, and realizes efficient and accurate mobile webpage camouflage detection.
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