Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
<|reference_start|>Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation: 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.<|reference_end|>
@article{kumar2024towards,
    title={Towards Robust Real-Time Hardware-based Mobile Malware Detection using
  Multiple Instance Learning Formulation},
    author={Harshit Kumar, Sudarshan Sharma, Biswadeep Chakraborty, Saibal
  Mukhopadhyay},
    journal={arXiv preprint arXiv:2404.13125},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13125},
    primaryClass={cs.CR cs.LG}
}