REMEDII: Robust Malware Detection with Iterative and Intelligent Adversarial Training

Malware detection traditionally relies on signature-based approaches, which suffer from limited generalization. To mitigate this issue, machine learning (ML)-based detection methods have been integrated with signature-based methods in recent years. However, ML-based detectors are vulnerable to adversarial attacks, particularly those leveraging Generative Adversarial Networks (GAN) to alter the features of malware while retaining its malicious purpose and bypassing detection. In this paper, we present an innovative defense mechanism called REMEDII (Robust Malware Detection with Iterative Adversarial Training), which improves the robustness of ML-based malware detectors.