| Autor: |
Mohanty, Sanjukta, Acharya, Arup Abhinna |
| Předmět: |
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| Zdroj: |
Grenze International Journal of Engineering & Technology (GIJET); 2021, Vol. 7 Issue 1, p648-653, 6p |
| Abstrakt: |
The usage of web application is increased in a rapid rate which leads to different kinds of cyber attacks. Cross Site Scripting (XSS) is one of the application layer code injection attack of web application which allows the invader to execute the malicious code (typically JavaScript) in a victim's browser to steal the cookies, sensitive data, spread malwares, deface the web page, hijack the session etc. Malicious JavaScript and malicious links are the most usual ways of performing the XSS attack. So identifying the script as malicious or benign is an important issues in security testing of web application. In this research article we survey the different effective detection technique for XSS using machine learning algorithms. This study employs static detection with machine learning approaches to analyze and classify the JavaScript malware features to be used in different machine learning classifiers and ensemble machine learning techniques for the prediction of XSS attack. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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