Machine learning-based xss defense system and realization method thereof

Abstract

The invention discloses a machine learning-based xss defense system and a realization method thereof. The system comprises a data packet capturing module, a machine training module, a feature value extraction module, a feature value matching module, a machine learning module, a feature value processing module and a log record module which are sequentially connected. In the machine learning-based xss defense system of the invention, monitoring of xss attacks is carried out on a defended target, machine learning is carried out on strange xss attack code feature values, latest xss feature valuescan be quickly and automatically collected in cloud-side xss attack defense, manual collection of social engineering information is eliminated, and high effectiveness and rapidity of xss defense are realized.

Description

technical field [0001] The invention relates to the field of computer software information technology, in particular to an xss defense system based on machine learning and its implementation method. Background technique [0002] In recent years, with the rapid development of network and computer software, more and more problems about information security have been exposed. Correspondingly, people have paid more attention to and paid more attention to network security than before. Security Testing has also gradually become more important. [0003] In recent years, network attacks caused by web applications are the main threats in web servers, and xss attacks, as common web attacks, are also one of the most important threats. Therefore, xss attacks have naturally become a must in security testing. Indispensable detection items. [0004] Security testers mainly attack a website by inputting xss attack codes to verify whether the website filters the data entered by users. If t...

AI Technical Summary

Problems solved by technology

And the current xss rule base only contains some special characters or some common simple xss attack codes, without the function of automatically learning and updating the xss filtering rule base, so that attackers can bypass the server-side in other more flexible ways Filter, and finally execute the script customized by the attacker in the user's browser to achieve the purpose of attacking the user

This leads to certain limitations in the filtering mechanism of xss defense, and also brings certain limitations to security testers

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