AI-enhanced vulnerability detection: A machine learning approach to automated penetration testing

Abstract

Cybersecurity is an issue that is being compounded by the fast development of digital infrastructure as cyberattacks grow more sophisticated and frequent. Vulnerability detection and penetration testing are some of the most important challenges in the area of cybersecurity as they allow organizations to highlight the security vulnerabilities prior to the exploitation. The existing traditional penetration testing is, however, largely manual, expensive, inefficient, and time-consuming, especially in large scale dynamic networks. This paper presents an improved vulnerability detection system using AI that can be used to automate penetration testing to enhance the ability of the system to detect threats more effectively. It incorporates automated attack path exploration, AI based threat intelligence and adversarial defense to increase the precision, versatility and dependability of penetration tests. To make security assessment transient and interpretable, explainable-AI methods are also included. Findings show that AI-based penetration testing also saves considerable time in human work, and it detects high-risk threats more efficiently through the analysis of large datasets, including complicated attack patterns. This piece is emphasizing a change to the conventional way of approaching cybersecurity as it provides a more effective and scalable alternative to the conventional way of doing it.