Hardware-Assisted Malware Detection using Machine Learning

DATE 2021

Zhixin Pan Jennifer Sheldon Chamika Sudusinghe Subodha Charles Prabhat Mishra

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Design Automation and Test in Europe (DATE), Grenoble, France, February 1-5, 2021


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, software-based 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.



A researcher (University of Moratuwa), entrepreneur (Pearl Cluster) and a volunteer (IEEE).

© 2023 Subodha Charles. All rights reserved.