Application of Deep Reinforcement Learning (DRL) for Malware Detection

Abstract

Malware poses a significant threat to computer systems and networks, necessitating advanced detection methods to safeguard against potential cyber-attacks. This paper investigates the application of Deep Reinforcement Learning (DRL) for malware detection, leveraging its ability to learn complex patterns and behaviours from raw data. The study employs a DRL framework to train an agent to identify malicious software based on dynamic features extracted from executable files. A comprehensive evaluation is conducted using a diverse dataset, encompassing various types of malware samples. The experimental results demonstrate the effectiveness of the proposed DRL based approach in accurately detecting malware, achieving competitive performance compared to traditional methods and state-of-the-art techniques. Additionally, the paper discusses the interpretability and scalability of the model, along with potential challenges and future research directions in applying DRL to cybersecurity.