EnsembleHMD: Accurate Hardware Malware Detectors with Specialized Ensemble Classifiers

Abstract

Hardware-based malware detectors (HMDs) are a promising new approach to defend against malware. HMDs collect low-level architectural features and use them to classify malware from normal programs. With simple hardware support, HMDs can be always on, operating as a first line of defense that prioritizes the application of more expensive and more accurate software-detector. In this paper, our goal is to increase the accuracy of HMDs, to improve detection, and reduce overhead. We use specialized detectors targeted towards a specific type of malware to improve the detection of each type. Next, we use ensemble learning techniques to improve the overall accuracy by combining detectors. We explore detectors based on logistic regression (LR) and neural networks (NN). The proposed detectors reduce the false-positive rate by more than half compared to using a single detector, while increasing their sensitivity. We develop metrics to estimate detection overhead; the proposed detectors achieve more than 16.6x overhead reduction during online detection compared to an idealized software-only detector, with an 8x improvement in relative detection time. NN detectors outperform LR detectors in accuracy, overhead (by 40 percent), and time-to-detection of the hardware component (by 5x). Finally, we characterize the hardware complexity by extending an open-core and synthesizing it on an FPGA platform, showing that the overhead is minimal.