  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.
