algoXSSF: Detection and analysis of cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks via Machine learning algorithms
Naresh Kshetri, Dilip Kumar, James Hutson, Navneet Kaur, Omar Faruq, Osama

TL;DR
This paper introduces algoXSSF, a machine learning-based framework for detecting and analyzing cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks to enhance web security.
Contribution
The paper presents a novel machine learning algorithm and cyber defense framework, algoXSSF, specifically designed for detecting and analyzing XSRF and XSS attacks.
Findings
Effective detection of XSRF and XSS attacks using machine learning.
Improved cyber attack analysis and trend identification.
Framework enhances web security against malicious threats.
Abstract
The global rise of online users and online devices has ultimately given rise to the global internet population apart from several cybercrimes and cyberattacks. The combination of emerging new technology and powerful algorithms (of Artificial Intelligence, Deep Learning, and Machine Learning) is needed to counter defense web security including attacks on several search engines and websites. The unprecedented increase rate of cybercrime and website attacks urged for new technology consideration to protect data and information online. There have been recent and continuous cyberattacks on websites, web domains with ongoing data breaches including - GitHub account hack, data leaks on Twitter, malware in WordPress plugins, vulnerability in Tomcat server to name just a few. We have investigated with an in-depth study apart from the detection and analysis of two major cyberattacks (although…
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Taxonomy
TopicsWeb Application Security Vulnerabilities · Network Security and Intrusion Detection · Advanced Malware Detection Techniques
