PENETRATION TESTING USING DEEP REINFORCEMENT LEARNING

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 o