# Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation

arXiv: https://arxiv.org/abs/2404.13125 · HTML: https://mlsanity.com/paper/2404.13125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Towards Improving the Trustworthiness of Hardware based Malware Detector using Online Uncertainty Estimation](https://mlsanity.com/paper/2103.11519/) (2021)
- [XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection](https://mlsanity.com/paper/2206.12447/) (2022)

## Abstract (arXiv)

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.