mlsanityData to 7 Oct 2026

Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling

arXiv 2103.06936 ↗First posted 11 Mar 2021cs.CR cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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 a novel HMDs (Stochastic-HMDs) through approximate computing, which makes HMDs' inference computation-stochastic, thereby making HMDs resilient against adversarial evasion attacks. Specifically, we propose to leverage voltage overscaling to induce stochastic computation in the HMDs model. We show that such a technique makes HMDs more resilient to both black-box adversarial attack scenarios, i.e., reverse-engineering and transferability. Our experimental results demonstrate 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. Moreover, based on recent results in probably approximately correct (PAC) learnability theory, we show that Stochastic-HMDs are provably more difficult to reverse engineer.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 56 references that are in the map. First posted on arXiv 11 Mar 2021.

Builds on

19 of 56 references.

References · 19

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  3. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  4. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  5. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  6. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 462
  7. 2018Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Modelscited by 362
  8. 2017Certified Defenses for Data Poisoning Attackscited by 288
  9. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  10. 2018Stochastic Activation Pruning for Robust Adversarial Defensecited by 171
  11. 2017Towards Robust Neural Networks via Random Self-ensemblecited by 158
  12. 2014Towards Deep Neural Network Architectures Robust to Adversarial Examplescited by 146
7 more
  1. 2017Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compressioncited by 86
  2. 2018AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learningcited by 58
  3. 2017Biologically inspired protection of deep networks from adversarial attackscited by 23
  4. 2018Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attackscited by 13
  5. 2019Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defensescited by 8
  6. 2018Towards Understanding Limitations of Pixel Discretization Against Adversarial Attackscited by 3
  7. 2016EMMA: A New Platform to Evaluate Hardware-based Mobile Malware Analysescited by 2

Built on by

Not cited yet.