Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response

2024-08-04

arXiv.org

AI Summary

To address the low efficiency and heavy reliance on manual analysis in post-incident malware forensics, this paper proposes a reinforcement learning framework based on Markov Decision Processes (MDPs). The forensic process is formalized as a state-action-reward sequence, where Q-learning and temporal-difference methods—combined with an ε-greedy policy—are employed for feature-driven evidence parsing. Innovatively, the framework introduces structured MDP modeling and an adaptive learning rate mechanism to enable continuous threat-pattern learning and dynamic response to evolving threats. Experimental results demonstrate that the method significantly reduces forensic analysis time, outperforms human experts in accuracy and speed, and exhibits strong robustness and generalization across diverse, complex cybersecurity scenarios—including zero-day attacks, multi-stage intrusions, and obfuscated malware families.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Cyber-crime defenses and forensics

Abstract

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.

Problem

• Reinforcement Learning
• Cybersecurity
• Malware Detection

Innovation

• Reinforcement Learning
• Q-Learning
• Adaptive Malware Detection