# Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation

arXiv: https://arxiv.org/abs/2103.11519 · HTML: https://mlsanity.com/paper/2103.11519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Defending Hardware-based Malware Detectors against Adversarial Attacks](https://mlsanity.com/paper/2005.03644/) (2020)

## Built on by (most cited first, 1 of 1)

- [Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation](https://mlsanity.com/paper/2404.13125/) (2024)

## Abstract (arXiv)

Hardware-based Malware Detectors (HMDs) using Machine Learning (ML) models have shown promise in detecting malicious workloads. However, the conventional black-box based machine learning (ML) approach used in these HMDs fail to address the uncertain predictions, including those made on zero-day malware. The ML models used in HMDs are agnostic to the uncertainty that determines whether the model "knows what it knows," severely undermining its trustworthiness. We propose an ensemble-based approach that quantifies uncertainty in predictions made by ML models of an HMD, when it encounters an unknown workload than the ones it was trained on. We test our approach on two different HMDs that have been proposed in the literature. We show that the proposed uncertainty estimator can detect >90% of unknown workloads for the Power-management based HMD, and conclude that the overlapping benign and malware classes undermine the trustworthiness of the Performance Counter-based HMD.