A malicious domain name research and judgment method based on a search engine and a generative language model

By combining search engines and generative language models, domain names are judged from multiple dimensions and manually reviewed. The malicious domain name model is optimized, which solves the problems of low efficiency and high false positive rate of traditional detection methods, and achieves efficient and accurate identification and judgment of malicious domain names.

CN119675890BActive Publication Date: 2026-06-23BEIJING ACT TECH DEV CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING ACT TECH DEV CO LTD
Filing Date
2024-10-15
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Traditional methods for detecting malicious domains are inefficient and have a high false positive rate, making it difficult to cope with constantly changing malicious domain strategies and unable to effectively identify and assess malicious domains.

Method used

By combining search engines and generative language models, we conduct in-depth analysis of domain names. Through multi-dimensional judgment results and manual review, we optimize the large-scale malicious domain name model to improve the accuracy and efficiency of identification and judgment.

Benefits of technology

It significantly improves the efficiency and accuracy of malicious domain name analysis, reduces the false alarm rate, effectively identifies new types of malicious domain names, reduces the workload of manual analysis, and enhances network security defense capabilities.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a malicious domain name research and judgment method based on a search engine and a generative language model. The method analyzes domain names in depth through a search engine and a generative language model, correlates internal malicious intelligence, and makes a judgment. The multi-dimensional judgment result is obtained by combining the real-time analysis result of the model and the internal intelligence analysis result. Then, the final domain name research and judgment result data is entered into a domain name research and judgment result database, and the model training data set is extracted from the final domain name research and judgment result data to optimize the malicious domain name large model for identifying and researching malicious domain names, improving the accuracy and efficiency of research and judgment. Compared with the traditional malicious domain name detection method, the present application greatly improves the efficiency and accuracy of research and judgment by combining a search engine and a generative language model, and reduces the false positive rate. The method can effectively identify new malicious domain names, reduce the workload of manual analysis, and improve the overall network security defense capability.
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