Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.13125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913322492755968 |
|---|---|
| author | Kumar, Harshit Sharma, Sudarshan Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| author_facet | Kumar, Harshit Sharma, Sudarshan Chakraborty, Biswadeep Mukhopadhyay, Saibal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13125 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation Kumar, Harshit Sharma, Sudarshan Chakraborty, Biswadeep Mukhopadhyay, Saibal Cryptography and Security Machine Learning 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. |
| title | Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2404.13125 |