Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation
TimeWednesday, December 8th3:30pm - 3:41pm PST
Embedded and Cross-Layer Security
DescriptionHardware-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. We test our approach on two different HMDs that have been proposed in the literature.