Leveraging Reinforcement Learning for an Efficient Windows Registry Analysis during Cyber Incident Response

Microsoft Windows is a widely deployed operating system, making forensic analysis, notably registry analysis, a common practice and central task in cyber incident response. However, the growing complexity of modern cyberattacks, the rapid evolution of malware, and the vast scale of registry data all pose significant challenges to traditional forensic methods. Conventional tools for Windows registry forensics often require extensive manual effort, lack adaptability, and may miss subtle but critical forensic artefacts. This paper introduces WinRegRL, a registry analysis framework that combines Reinforcement Learning with Rule-based Artificial Intelligence. It models forensic analysis as a Markov Decision Process mimicking the sequential decision-making operated by experts during incident response. Extensive testing across multiple datasets, including real-world capture-the-flag challenges, demonstrated that WinRegRL outperforms leading forensic tools and experienced, human-certified examiners. The framework reduced investigation time by up to 68%, increased the number of relevant Windows registry artefacts identified by up to 35%, and achieved a consistent accuracy rate above 99% validated across various datasets, confirming our approach’s scalability, adaptability, and generalizability. By combining automated learning with expert-informed reasoning, WinRegRL enables faster, more accurate, and repeatable Windows registry investigations, providing substantial benefits for large-scale incident response operations, multi-system incident response, and time-critical cybercrime investigations that require extensive coverage and forensic precision.