Cross-Site Scripting Prevention Using Machine Learning Regarding the JavaScript Esoteric Paradigm

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Safari Ali; Manavi Farnoush; HAMZEH ALI

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

Cross-Site Scripting Prevention Using Machine Learning Regarding the JavaScript Esoteric Paradigm

Author(s)

Safari Ali | Manavi Farnoush | HAMZEH ALI

Keywords

Cross Site Scripting

JavaScript

Machine Learning

XSS

Abstract

In recent years, billions of users are exchanging information at an increasing rate in the vast and interconnected world of the Internet. On the other hand, attackers keep planning various threats in this environment. As a result of these issues, it has become more and more critical to prevent or reduce vulnerabilities. Cross Site Scripting is a well-known vulnerability on the web through which an intruder tries to steal users’,vital information or induce malicious activities on behalf of the user. The weakness of the previous methods of detecting XSS attacks based on Machine Learning lies in not taking the possible changes in the characters of the attack vector into account, or in other words, special encodings, and this reduces the accuracy of these methods. The method presented in this article utilizes an algorithm that translates a kind of obfuscation in the attack vector that increases the accuracy of the detection model on the XSSED dataset to over 98%.

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APA: Copy

Safari, Ali, Manavi, Farnoush, & HAMZEH, ALI. (). . . SID. https://sid.ir/paper/1046818/en

Vancouver: Copy

Safari Ali, Manavi Farnoush, HAMZEH ALI. . . Available from: https://sid.ir/paper/1046818/en

IEEE: Copy

Ali Safari, Farnoush Manavi, and ALI HAMZEH, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1046818/en

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