Automated Penetration Testing Through Reinforcement Learning
Fatimah Alghamdi · 2025
Automated penetration testing is essential as cyber threats become more complex, outpacing traditional manual methods. This chapter explores the integration of reinforcement learning (RL) in penetration testing, emphasizing its ability to adapt to dynamic environments and improve testing efficiency. RL models optimize attack strategies and simulate complex, real-world attack scenarios, surpassing manual methods. The chapter covers RL components, algorithms, and their specific application in cybersecurity, particularly how RL can be integrated with attack graphs to model attack paths and identify vulnerabilities more effectively. Combining RL with attack graphs enhances penetration testing by automating vulnerability detection and optimizing attack sequences. Challenges such as computational cost, reward sparsity, and real-world applicability are discussed, alongside RL's potential to scale and improve cybersecurity measures. Future research aims to address these challenges, enabling more efficient, adaptable, and automated penetration testing frameworks.