DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
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| Publicado no: | arXiv.org (Aug 29, 2024), p. n/a |
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| Autor principal: | |
| Outros Autores: | , , , , |
| Publicado em: |
Cornell University Library, arXiv.org
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full text outside of ProQuest |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3098950902 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3098950902 | ||
| 045 | 0 | |b d20240829 | |
| 100 | 1 | |a Sun, Tiezhu | |
| 245 | 1 | |a DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware | |
| 260 | |b Cornell University Library, arXiv.org |c Aug 29, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Recent advancements in ML and DL have significantly improved Android malware detection, yet many methodologies still rely on basic static analysis, bytecode, or function call graphs that often fail to capture complex malicious behaviors. DexBERT, a pre-trained BERT-like model tailored for Android representation learning, enriches class-level representations by analyzing Smali code extracted from APKs. However, its functionality is constrained by its inability to process multiple Smali classes simultaneously. This paper introduces DetectBERT, which integrates correlated Multiple Instance Learning (c-MIL) with DexBERT to handle the high dimensionality and variability of Android malware, enabling effective app-level detection. By treating class-level features as instances within MIL bags, DetectBERT aggregates these into a comprehensive app-level representation. Our evaluation demonstrates that DetectBERT not only surpasses existing state-of-the-art detection methods but also adapts to evolving malware threats. Moreover, the versatility of the DetectBERT framework holds promising potential for broader applications in app-level analysis and other software engineering tasks, offering new avenues for research and development. | |
| 653 | |a Threat evaluation | ||
| 653 | |a Malware | ||
| 653 | |a Software engineering | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Learning | ||
| 653 | |a Applications programs | ||
| 653 | |a Graphical representations | ||
| 653 | |a State-of-the-art reviews | ||
| 653 | |a Computer programming | ||
| 700 | 1 | |a Daoudi, Nadia | |
| 700 | 1 | |a Kim, Kisub | |
| 700 | 1 | |a Allix, Kevin | |
| 700 | 1 | |a Bissyandé, Tegawendé F | |
| 700 | 1 | |a Klein, Jacques | |
| 773 | 0 | |t arXiv.org |g (Aug 29, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3098950902/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2408.16353 |