  This research focused on enhancing post-incident malware forensic
investigation using reinforcement learning RL. We proposed an advanced MDP post
incident malware forensics investigation model and framework to expedite post
incident forensics. We then implement our RL Malware Investigation Model based
on structured MDP within the proposed framework. To identify malware artefacts,
the RL agent acquires and examines forensics evidence files, iteratively
improving its capabilities using Q Table and temporal difference learning. The
Q learning algorithm significantly improved the agent ability to identify
malware. An epsilon greedy exploration strategy and Q learning updates enabled
efficient learning and decision making. Our experimental testing revealed that
optimal learning rates depend on the MDP environment complexity, with simpler
environments benefiting from higher rates for quicker convergence and complex
ones requiring lower rates for stability. Our model performance in identifying
and classifying malware reduced malware analysis time compared to human
experts, demonstrating robustness and adaptability. The study highlighted the
significance of hyper parameter tuning and suggested adaptive strategies for
complex environments. Our RL based approach produced promising results and is
validated as an alternative to traditional methods notably by offering
continuous learning and adaptation to new and evolving malware threats which
ultimately enhance the post incident forensics investigations.
