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Malicious website identification method and system based on reinforcement learning and medium

A malicious website and reinforcement learning technology, applied in the field of malicious website identification based on reinforcement learning, can solve problems such as weak robustness, improve robustness and accuracy, and reduce the risk of data leakage and property loss

Active Publication Date: 2021-08-27
CHINA ACADEMY OF INFORMATION & COMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, using the nonlinear transformation of the support vector machine and the principle of structural risk minimization to improve the generalization ability of the classifier has good classification accuracy and stability, but the classification results are not robust to the distribution of the training set and parameter configuration

Method used

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  • Malicious website identification method and system based on reinforcement learning and medium
  • Malicious website identification method and system based on reinforcement learning and medium
  • Malicious website identification method and system based on reinforcement learning and medium

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Embodiment Construction

[0041] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Embodiments of the present disclosure will be described in further detail below in conjunction with the accompanying drawings and specific embodiments, but it is not intended to limit the present disclosure. For the various steps described herein, if there is no need for a contextual relationship between each other, the order described herein as an example should not be considered as a limitation, and those skilled in the art will know that the order can be adjusted, as long as It is enough not to destroy the logic between them so that the whole process cannot be realized.

[0042] A scheme of a malicious website identification method based on reinforcement learning according to an embodiment of the present disclosure will be des...

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Abstract

The invention relates to a malicious website identification method and system based on reinforcement learning and a medium. The method comprises the steps: S1, receiving a kth website, wherein the kth website is researched and judged through a domain name blacklist, the kth website is a website to be researched and judged, and k is a positive integer; S2, under the condition that the domain name of the kth website is not in the domain name blacklist, determining the priority of the kth website, and determining a feature vector of the kth website based on the priority; S3, determining a test statistic delta based on the feature vector of the kth website, and studying and judging the kth website according to a comparison result of the test statistic delta and the studying and judging threshold value theta k, wherein k is a positive integer, and N is a positive integer smaller than k.

Description

technical field [0001] The present disclosure relates to the field of malicious website identification, and more specifically, to a method, system and medium for identifying malicious website based on reinforcement learning. Background technique [0002] Cyber ​​attackers use malicious webpages, such as phishing webpages, Trojan horse webpages, and spam advertisement webpages, to steal user sensitive information, promote malicious software and spam advertisements, and make illegal profits. Malicious webpages seriously endanger users' information data and property security, and the research, judgment and identification of malicious webpages is an urgent Internet security problem to be solved. [0003] Malicious webpage identification methods mainly include identification methods based on blacklist technology, identification methods based on heuristic rules, and identification methods based on machine learning. The identification method based on blacklist technology mainly ut...

Claims

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Application Information

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IPC IPC(8): H04L29/06G06N20/00
CPCH04L63/101H04L63/1416H04L63/1483G06N20/00
Inventor 万晓玥崔现东杜伟王玉环董亚萍
Owner CHINA ACADEMY OF INFORMATION & COMM
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