PENETRATION TESTING USING DEEP REINFORCEMENT LEARNING

Abstract

Traditionally, penetration testing is performed by experts who manually simulate attacks on computer networks to assess their security and identify vulnerabilities. However, recent research highlights the significant potential for automating this process through deep reinforcement learning. The development of automated testing systems promises to significantly increase the accuracy, speed and efficiency of vulnerability detection and remediation. In the pre-testing phase, artificial intelligence can be used to automatically create a realistic network topology, including the development of a tree of possible attacks. The use of deep learning methods, such as Deep Q-Learning, allows the system to determine the best attack paths, making the penetration process more strategic and informed. Automated penetration testing systems can serve as effective training tools for cybersecurity professionals. They allow attacks to be simulated in a controlled training environment, providing users with the opportunity to analyse different intrusion strategies and techniques, and serve as a training tool for detecting and responding to real-world attacks. This approach promotes a deep understanding of potential threats and develops the skills to effectively defend against them. In addition, the use of machine learning can help solve the problem of large numbers of false positives, which is a common problem in traditional security systems. Deep reinforcement learning offers the opportunity to create more adaptive scanning systems that can learn and adapt to changing threat patterns. Such systems are not only more efficient, but also able to operate with fewer errors, reducing the burden of human error. As a result, they can identify vulnerabilities that humans may not, providing a deeper and more comprehensive security analysis. This approach has the potential to revolutionise the cybersecurity industry, offering new strategies for protecting information systems and creating more robust network structures.