Operations Investigation, George Mason University, Fairfax, VA 22030, USA; [email protected]
Operations Analysis, George Mason University, Fairfax, VA 22030, USA; [email protected] Department of Pc 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 Wonderful 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 Seclidemstat mesylate run-time Hardware-Assisted Stealthy Malware Detection: A Lightweight, yet Efficient Time Series CNN-Based Method. Cryptography 2021, 5, 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. Licensee MDPI, Basel, Switzerland. This article is definitely an open access short article distributed under the terms and situations of your Inventive Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Abstract: According to recent security Bomedemstat Autophagy Analysis reports, malicious software program (a.k.a. malware) is rising at an alarming price in numbers, complexity, and damaging purposes to compromise the safety of contemporary personal computer systems. Lately, malware detection based on low-level hardware capabilities (e.g., Hardware Performance Counters (HPCs) data) has emerged as an effective alternative resolution to address the complexity and functionality overheads of classic software-based detection techniques. Hardware-assisted Malware Detection (HMD) techniques depend on standard Machine Learning (ML) classifiers to detect signatures of malicious applications by monitoring built-in HPC registers through execution at run-time. Prior HMD procedures even though successful have limited their study on detecting malicious applications which might be spawned as a separate thread during application execution, hence detecting stealthy malware patterns at run-time remains a critical 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. Within this paper, we initial present a comprehensive review of recent advances in hardware-assisted malware detection studies that have utilized normal 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 utilizing branch directions, by far the most prominent HPC feature. StealthMiner is primarily based on a lightweight time series Fully Convolutional Neural Network (FCN) model that automatically identifies potentially contaminated samples in HPC-based time series information and utilizes them to accurately recognize the trace of stealthy malware. Our evaluation demonstrates that making use of state-of-the-art ML-based malware detection techniques is not effective in detecting stealthy malware samples since the captured HPC data not simply represents malware but also carries benign applications’ microarchitectural information. The experimental benefits demonstrate that with all the help of our novel intelligent strategy, stealthy malware may be detected at run-time with 94 detection efficiency on typical with only one particular HPC feature, outperforming th.