Stochastic-HMDs: adversarial-resilient hardware malware detectors via undervolting

Md Shohidul Islam*, Ihsen Alouani, Khaled N. Khasawneh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
70 Downloads (Pure)

Abstract

Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. However, HMDs suffer from adversarial attacks, can be effectively reverse-engineered and subsequently be evaded, allowing malware to hide from detection. We address this issue by proposing novel HMDs (Stochastic-HMDs), which leverage approximate computing (AC) to harden HMDs against adversarial evasion attacks. Stochastic-HMDs introduce stochastic noise into the computations within the model to build an efficient and low-cost moving-target defense. Specifically, we use controlled undervolting, i.e., scaling the supply voltage below nominal level, to deliberately induce stochastic timing violations in the HMDs' computations during inference (detection). We show that such technique makes HMDs more resilient to adversarial attacks, especially to reverse-engineering and transferability. Our thorough empirical results substantiate that Stochastic-HMDs offer effective defense against adversarial attacks along with by-product power savings, without requiring any changes to the hardware/software nor to the HMDs' model, i.e., no retraining or fine tuning is needed. In particular, Stochastic-HMDs can detect more than 94% of the evasive malware with a negligible (i.e., < 2%) accuracy loss, along with ~15% power savings.

Original languageEnglish
Title of host publication2023 60th ACM/IEEE Design Automation Conference, DAC 2023: proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350323481
ISBN (Print)9798350323498
DOIs
Publication statusPublished - 15 Sept 2023
Event60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States
Duration: 09 Jul 202313 Jul 2023

Publication series

NameProceedings - Design Automation Conference
Volume2023-July
ISSN (Print)0738-100X

Conference

Conference60th ACM/IEEE Design Automation Conference, DAC 2023
Country/TerritoryUnited States
CitySan Francisco
Period09/07/202313/07/2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Adversarial Attack
  • HMD
  • Undervolting

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modelling and Simulation

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