Domain name security detection method based on multi-dimensional data association analysis

CN119966730APending Publication Date: 2025-05-09BEIJING EASYNETWORKS TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510145940.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-10
Publication Date
2025-05-09

AI Technical Summary

Technical Problem

Existing domain name security detection methods rely on single features or simple rules, resulting in a lack of comprehensiveness and accuracy in detection results. It is especially difficult to evaluate the security of newly registered or historical data, and cannot meet the needs of large-scale domain name detection.

Method used

The domain name security detection method based on multidimensional data association analysis is adopted. By collecting multidimensional data (including domain name registration information, analysis records, historical behavior, naming rules and categories), multiple features are extracted, feature vectors are constructed, and a pre-trained machine learning model is input for security scores. Finally, the domain name security is judged based on the score and the preset threshold.

Benefits of technology

Through the comprehensive analysis of multi-dimensional data, potential malicious domain names can be more accurately identified, detection accuracy and efficiency can be improved, large-scale domain name detection needs can be adapted to the needs of large-scale domain name detection, and dynamically adjusted to deal with complex network security threats.

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Abstract

The invention relates to the field of domain name security detection, and discloses a domain name security detection method based on multi-dimensional data association analysis, and the method comprises the following steps: 1, collecting multi-dimensional data of a domain name to be detected, the multi-dimensional data comprises domain name registration information, domain name resolution records, historical behavior data, domain name naming rules and domain name categories; step 2, extracting a plurality of features of the domain name to be detected, wherein the features comprise domain name length, character randomness, historical behavior, domain name age, registration place and domain name category; step 3, constructing a feature vector of the domain name to be detected according to the plurality of features; and step 4, inputting the feature vector into a pre-trained domain name security detection model. Through comprehensive analysis of multi-dimensional data, multiple factors such as registration information, analysis records, historical behaviors, domain name naming rules and domain name categories of domain names are considered, and the problem that security assessment is not comprehensive due to the fact that an existing method only depends on a single feature is solved.
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Citation Information

Patent Citations

  • Malicious domain name detection method based on SVM machine learning

    CN110866611A

  • Method for multi-dimensionally detecting malicious domain names

    CN111245784A

  • Malicious domain name identification method based on multiple types of features

    CN118540076A