JAMXSS: An Advanced Machine Learning-Powered Scanner for Context-Aware Detection and Exploitation of Reflected XSS Vulnerabilities

JAMXSS: An Advanced Machine Learning-Powered Scanner for Context-Aware Detection and Exploitation of Reflected XSS Vulnerabilities
Author(s):
Suriya Prakash Nagarajan, Dr. Govindraj Pandith T G, Prof. Sreenivasa H V
Keywords:
Context-Aware Detection, Machine Learning, Payload Generation, XSS Scanner
Abstract
Cross-Site Scripting (XSS) vulnerabilities pose a significant threat to web application security, often resulting in severe breaches. Traditional XSS detection methods, relying on brute-forcing payloads, are time-consuming and resource-intensive. This paper presents JAMXSS (Just A Monster XSS Scanner), an advanced tool designed to enhance XSS vulnerability detection using machine learning techniques. JAMXSS improves detection efficiency by predicting and analyzing the context of reflections within web applications, generating context-specific payloads. The tool integrates components such as a crawler for URL collection, a reflection tester, a context analyzer, and a payload generator. Evaluation results from controlled environments and real-world applications demonstrate JAMXSS's effectiveness in identifying vulnerabilities with high accuracy. By combining machine learning with innovative detection and payload generation methods, JAMXSS offers a robust solution for mitigating XSS vulnerabilities.
Article Details
Unique Paper ID: 167184 Publication Volume & Issue: Volume 11, Issue 3 Page(s): 532 - 536
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