Reinforcement Learning Model Developed for Malware Investigations
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🦠 Novel Reinforcement Learning Model Enhances Malware Forensics Efficiency. A new research study introduces a Reinforcement Learning (RL) model designed to optimize malware forensics during cyber incident responses. By utilizing Q-learning and Markov Decision Processes, the model aims to reduce false negatives and adapt to evolving malware signatures, automating the analysis of malware patterns in live memory dumps. The framework is guided by a comprehensive malware workflow and employs both static and behavioral techniques alongside machine learning algorithms. Experimental results indicate that the RL model significantly improves malware detection rates compared to traditional methods, although its effectiveness varies with the complexity of the environment. The study emphasizes the need for continuous refinement of reward systems and feature extraction to enhance the model’s performance across diverse malware types.
