AlgoXSSF: Detection and Analysis of Cross-Site Request Forgery (XSRF) and Cross-Site Scripting (XSS) Attacks via Machine Learning Algorithms

The global rise in online users and devices has led to a corresponding surge in cybercrimes and attacks, demanding advanced technology and algorithms like Artificial Intelligence (AI), Deep Learning, and Machine Learning to bolster web security. 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. Extensive research has been conducted to detect and analyze prevalent cyber threats like cross-site request forgery (XSRF) and cross-site scripting (XSS) attacks. Leveraging machine learning algorithms, particularly through the developed cyber defense framework - algoXSSF, offers numerous benefits. AlgoXSSF enhances detection accuracy, facilitates real-time threat response, autonomously adapts to evolving threats, minimizes false positives, and streamlines security operations. By harnessing the power of machine learning, algoXSSF serves as a robust defense mechanism against malicious attacks, including Man-in-the-Middle attacks, reinforcing websites and online platforms against cyber threats.