ObfusGate: Representation Learning-Based Gatekeeper for Hardware-Level Obfuscated Malware Detection

In this paper, we explore the interplay between code obfuscation techniques and performance counter traces to undermine Hardware Malware Detectors (HMDs) that rely on Machine Learning (ML) models. By crafting various obfuscated malware categories and analyzing a wide range of ML models, we demonstrate a notable detection performance reduction, showcasing the evasive impact of obfuscated malware in HMD methods. To counter these threats, we propose ObfusGate, an intelligent and robust defense mechanism based on feature representation learning that significantly enhances machine learning models against both obfuscated and unobfuscated malware attacks. The results indicate the effectiveness of Obfus Gate, attaining up to 24 % detection rate increase across diverse ML models assessed for hardware-level obfuscated malware detection at run-time.