Dynamic Malware Analysis using AI Agent and Reinforcement Learning

The exponential growth of sophisticated malware necessitates advanced automated analysis systems that go beyond traditional static signatures. Dynamic analysis, while robust, generates high-dimensional behavioral reports that are computationally expensive to process exhaustively. This paper introduces a novel Reinforcement Learning (RL) framework designed to optimize the dynamic analysis pipeline. We propose a custom AI agent powered by a Dueling Deep Q-Network (Dueling DQN) that interacts with the CAPEv2 sandbox in real-time. Unlike prior approaches that treat analysis as a passive classification task, we formulate it as a sequential decision-making problem where the agent selectively “reveals” portions of a behavioral report such as memory injections, network traffic, or filesystem operations only when necessary. Trained on a curated, diverse subset of the WinMET dataset comprising 536 samples (268 benign, 268 malware), our agent learns to balance the cost of information retrieval against classification accuracy. We present a full end-to-end implementation where the agent acts as an autonomous analyst within a virtualized Kali Linux/Windows 10 environment. Experimental results demonstrate an overall accuracy of 83.0% with precision of 83.78% and recall of 73.81% on malware detection. Crucially, the agent reduces the average number of analysis steps by 75.4% compared to exhaustive methods (4.93 vs 20 steps), demonstrating that intelligent, cost-aware agents can significantly alleviate the bottleneck in modern malware triage operations.