Operations Study, George Mason University, Fairfax, VA 22030, USA; [email protected]
Operations Study, George Mason University, Fairfax, VA 22030, USA; [email protected] Division of Computer system Science, University of California, Davis, CA 95616, USA; [email protected] Correspondence: [email protected] This operate is definitely an extended version of our paper published in Fantastic Lakes Symposium on VLSI (GLSVLSI 2020).Citation: Sayadi, H.; Gao, Y.; Mohammadi Makrani, H.; Lin, J.; Costa, P.C.; Rafatirad, S.; Homayoun, H. Towards Correct Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, however Effective Time Series CNN-Based Strategy. Cryptography 2021, five, 28. https://doi.org/10.3390/ cryptography5040028 Academic Editor: Jim Plusquellic Received: 3 October 2021 Accepted: 13 October 2021 Published: 17 OctoberPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Copyright: 2021 by the authors. Bomedemstat supplier Licensee MDPI, Basel, Switzerland. This short article is definitely an open access article distributed under the terms and circumstances of your Inventive Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Abstract: As outlined by recent security evaluation reports, malicious software program (a.k.a. malware) is increasing at an alarming price in numbers, complexity, and dangerous purposes to compromise the security of contemporary laptop or computer systems. Not too long ago, malware detection based on low-level hardware features (e.g., Hardware Overall performance Counters (HPCs) information and facts) has emerged as an effective alternative solution to address the complexity and overall performance overheads of classic software-based detection solutions. Hardware-assisted Malware Detection (HMD) strategies depend on regular Machine Finding out (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers for the duration of execution at run-time. Prior HMD techniques though helpful have limited their study on detecting malicious applications which can be spawned as a separate thread in the course of application execution, hence detecting stealthy malware patterns at run-time VBIT-4 web remains a important challenge. Stealthy malware refers to harmful cyber attacks in which malicious code is hidden within benign applications and remains undetected by traditional malware detection approaches. In this paper, we 1st present a extensive critique of current advances in hardware-assisted malware detection research which have used standard ML methods to detect the malware signatures. Next, to address the challenge of stealthy malware detection in the processor’s hardware level, we propose StealthMiner, a novel specialized time series machine learning-based strategy to accurately detect stealthy malware trace at run-time employing branch directions, the most prominent HPC feature. StealthMiner is based on a lightweight time series Completely Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series data and utilizes them to accurately recognize the trace of stealthy malware. Our analysis demonstrates that utilizing state-of-the-art ML-based malware detection solutions isn’t productive in detecting stealthy malware samples because the captured HPC data not just represents malware but additionally carries benign applications’ microarchitectural data. The experimental final results demonstrate that with the help of our novel intelligent strategy, stealthy malware could be detected at run-time with 94 detection functionality on average with only one HPC feature, outperforming th.