ActDroid: An active learning framework for Android malware detection
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914154603872256 |
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| author | Muzaffar, Ali Hassen, Hani Ragab Zantout, Hind Lones, Michael A |
| author_facet | Muzaffar, Ali Hassen, Hani Ragab Zantout, Hind Lones, Michael A |
| contents | The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android malware detection as a streaming data problem and explore the use of active online learning as a means of mitigating the problem of labelling applications in a timely and cost-effective manner. Our resulting framework achieves accuracies of up to 96\%, requires as little of 24\% of the training data to be labelled, and compensates for concept drift that occurs between the release and labelling of an application. We also consider the broader practicalities of online learning within Android malware detection, and systematically explore the trade-offs between using different static, dynamic and hybrid feature sets to classify malware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ActDroid: An active learning framework for Android malware detection Muzaffar, Ali Hassen, Hani Ragab Zantout, Hind Lones, Michael A Cryptography and Security Artificial Intelligence The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released. According to a recent study, a new piece of malware appears online every 12 seconds. To address this, we treat Android malware detection as a streaming data problem and explore the use of active online learning as a means of mitigating the problem of labelling applications in a timely and cost-effective manner. Our resulting framework achieves accuracies of up to 96\%, requires as little of 24\% of the training data to be labelled, and compensates for concept drift that occurs between the release and labelling of an application. We also consider the broader practicalities of online learning within Android malware detection, and systematically explore the trade-offs between using different static, dynamic and hybrid feature sets to classify malware. |
| title | ActDroid: An active learning framework for Android malware detection |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16982 |