Hardware-Assisted Malware Detection using Machine Learning
Zhixin Pan, Jennifer Sheldon, Chamika Sudusinghe, Subodha Charles and Prabhat Mishra
Department of Computer and Information Science and Engineering University of Florida, Gainesville, Florida, USA
ABSTRACT
Malicious software, popularly known as malware, is a serious threat to modern computing systems. A comprehensive cybercrime study by Ponemon Institute highlights that malware is the most expensive attack for organizations, with an average revenue loss of $2.6 million per organization in 2018 (11% increase compared to 2017). Recent high-profile malware attacks coupled with serious economic implications have dramatically changed our perception of threat from malware. Software-based solutions, such as anti-virus programs, are not effective since they rely on matching patterns (signatures) that can be easily fooled by carefully crafted malware with obfuscation or other deviation capabilities. Moreover, softwarebased solutions are not fast enough for real-time malware detection in safety-critical systems. In this paper, we investigate promising approaches for hardware-assisted malware detection using machine learning. Specifically, we explore how machine learning can be effective for malware detection utilizing hardware performance counters, embedded trace buffer as well as on-chip network traffic analysis.
Keywords: Malware Detection, Machine Learning, Hardware Performance Counters, Trace Buffer, On-Chip Traffic.
