Fraudulent website detection method and related devices

By crawling and extracting multi-dimensional website information and combining machine learning and deep learning methods, the problem of low efficiency and insufficient accuracy in the detection of fraudulent websites in existing technologies has been solved, achieving fast and efficient identification of fraudulent websites and risk reduction.

CN122268602APending Publication Date: 2026-06-23BEIJING HONGTENG INTELLIGENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN ¡ China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING HONGTENG INTELLIGENT TECH CO LTD
Filing Date
2024-12-19
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing technologies are insufficient to comprehensively and effectively identify and block fraudulent websites, especially in the face of sophisticated disguises and diverse methods, resulting in low detection efficiency and inadequate accuracy.

Method used

By crawling the website to be detected, extracting multi-dimensional website information, performing feature engineering, generating multi-dimensional website features, and inputting them into the website detection model for multi-modal detection, and combining machine learning and deep learning methods for classification judgment.

Benefits of technology

It improves the accuracy of detecting fraudulent websites, reduces false positives and false negatives, achieves rapid and efficient identification, enhances user security, and reduces risks.

✦ Generated by Eureka AI based on patent content.

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Abstract

Embodiments of the present application provide a fraudulent website detection method and related equipment. The fraudulent website detection method comprises: capturing a website to be detected, and extracting multi-dimensional website information; performing feature engineering on the multi-dimensional website information to obtain corresponding multi-dimensional website features; and inputting the multi-dimensional website features into a website detection model to obtain a detection result. Through the extraction and optimization of the multi-dimensional features of the website after capturing various website information, the present application provides multiple identification clues for the complex camouflage of fraudulent websites, further compresses redundant information, and strengthens key features related to fraud. These key features are input into the website detection model for multi-modal detection and classification, so as to efficiently and accurately determine whether the website is a fraudulent website. The present application not only can process complex multi-modal data, but also can quickly and efficiently identify fraudulent websites, improve detection accuracy, reduce misjudgment and omissions, and thus achieve the technical effects of improving user safety and reducing risks.
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