Stochastic-HMDs: Adversarial-Resilient Hardware Malware Detectors via Undervolting
Author:
Affiliation:
1. DUET,CSE Dept,Gazipur,Bangladesh
2. Queen’s University,CSIT,Belfast,UK
3. George Mason University,ECE Department,Fairfax,VA,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10247654/10247655/10247980.pdf?arnumber=10247980
Reference34 articles.
1. Efficient hardware malware detectors that are resilient to adversarial evasion;islam;IEEE Transactions on Computers,2021
2. Enhancing hardware malware detectors’ security through voltage over-scaling;islam;2021 5th ACM SIGARCH Workshop on Cognitive Architectures,0
3. Hardware Performance Counters Can Detect Malware
4. Monotonic-HMDs: Exploiting Monotonic Features to Defend Against Evasive Malware
5. ND-HMDs: Non-Differentiable Hardware Malware Detectors against Evasive Transient Execution Attacks
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2. Hardware Support for Trustworthy Machine Learning: A Survey;2024 25th International Symposium on Quality Electronic Design (ISQED);2024-04-03
3. Intelligent Malware Detection based on Hardware Performance Counters: A Comprehensive Survey;2024 25th International Symposium on Quality Electronic Design (ISQED);2024-04-03
4. SecureVolt: Enhancing Deep Neural Networks Security via Undervolting;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2023
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