Intelligent Malware Detection based on Hardware Performance Counters: A Comprehensive Survey
Hossein Sayadi, Zhangying He, Hosein Mohammadi Makrani, Houman Homayoun
Abstract
The growing complexity of contemporary computing systems heightens susceptibility to emerging cyber threats. Recent advancements in computer architecture security leverage Hardware Performance Counters (HPCs) registers to monitor applications behavior and access low-level features. The integration of Machine Learning (ML) techniques emerges as a promising solution, overcoming the performance limitations of conventional software-based defenses. Specialized HPC registers record varied hardware-related events, showcasing effectiveness in detecting malicious activities through the application of ML algorithms. This survey presents a comprehensive and comparative analysis of recent advancements in the emerging field of intelligent hardware-assisted malware detection, a topic that has garnered significant attention within the research community for the past decade. Additionally, it outlines current challenges and forecasts future research trends, offering insights for effective ML-based security countermeasures based on hardware performance counters.