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.

翻译：使用机器学习(ML)模型的基于硬件的恶意检测器(HMDs)在发现恶意工作量方面表现出了希望,然而,这些 HMDs中使用的基于黑盒的常规机器学习(ML)方法未能解决不确定预测,包括零天恶意软件的预测。 HMDs中使用的 ML模型对于确定模型是否“了解它知道什么”是否“了解”的不确定性是不可知的。我们建议采用基于共性的方法,在HMD模型遇到与所培训的工作量相比未知的工作量时,量化该模型预测的不确定性。我们测试了文献中提议的两种基于不同 HMD的方法。我们表明,拟议的不确定性估计器能够检测到以HMD为基础的电源管理未知工作量的>90%,并得出结论,重叠的良性和恶意等级会损害基于性能的反光谱的HMD的可信度。