AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm

This research introduces a new approach to enhancing cybersecurity by integrating Support Vector Machine (SVM) algorithms with penetration testing to develop a recommendation system focused on Cross-Site Scripting (XSS) attack detection. By leveraging AI and machine learning, the system dynamically suggests the most suitable penetration testing tools which are Nmap, XSStrike, PwnXSS, OWASP ZAP, or Burp Suite. The SVM algorithm, a supervised learning model, plays a crucial role in improving the efficiency of tool selection, ultimately enhancing the speed and adaptability of vulnerability detection processes. The research employs Extreme Programming (XP) methodologies to ensure high-quality code, agility, and collaborative development. The methodology focusing on simplicity and fast development process that suitable for simple and small project in a short time. The system is developed using Django as the main framework for Python on an Ubuntu server with SQLite as the database, utilizing a dataset synthesized in Microsoft Excel and aligned with real-world examples from Kaggle. Extensive testing, including unit, integration, and acceptance testing, has validated the system's functionality, although limitations such as a focus on XSS testing tools and some accuracy concerns were identified. This study contributes to the cybersecurity domain by offering a scalable, AI-driven solution that integrates traditional penetration testing practices with advanced machine learning techniques. Future work will explore expanding the system to include automated reporting, integration of ChatGPT, and support for a broader range of attack vectors, addressing current limitations and further enhancing the system's utility.