  This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for
mobile devices, that refines malware representation in segmented time-series
through a Multiple Instance Learning (MIL) approach. We address the mislabeling
issue in real-time HMDs, where benign segments in malware time-series
incorrectly inherit malware labels, leading to increased false positives.
Utilizing the proposed Malicious Discriminative Score within the MIL framework,
RT-HMD effectively identifies localized malware behaviors, thereby improving
the predictive accuracy. Empirical analysis, using a hardware telemetry dataset
collected from a mobile platform across 723 benign and 1033 malware samples,
shows a 5% precision boost while maintaining recall, outperforming baselines
affected by mislabeled benign segments.
