Detecting Blind Cross-Site Scripting Attacks Using Machine Learning

Gurpreet Kaur, Yasir Malik, Hamman W. Samuel, Fehmi Jaafar
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引用次数: 9

Abstract

Cross-site scripting (XSS) is a scripting attack targeting web applications by injecting malicious scripts into web pages. Blind XSS is a subset of stored XSS, where an attacker blindly deploys malicious payloads in web pages that are stored in a persistent manner on target servers. Most of the XSS detection techniques used to detect the XSS vulnerabilities are inadequate to detect blind XSS attacks. In this research, we present machine learning based approach to detect blind XSS attacks. Testing results help to identify malicious payloads that are likely to get stored in databases through web applications.
利用机器学习检测盲跨站脚本攻击
跨站脚本(XSS)是一种针对web应用程序的脚本攻击,通过在web页面中注入恶意脚本。盲目XSS是存储XSS的一个子集,攻击者在以持久方式存储在目标服务器上的网页中盲目地部署恶意有效负载。大多数用于检测XSS漏洞的XSS检测技术都不足以检测盲目的XSS攻击。在这项研究中,我们提出了一种基于机器学习的盲XSS攻击检测方法。测试结果有助于识别可能通过web应用程序存储在数据库中的恶意有效负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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