Multi-view Deep Learning for Zero-day Android Malware Detection

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Zero-day malware samples pose a considerable danger to users as implicitly there are no documented defences for previously unseen, newly encountered behaviour. Malware detection therefore relies on past knowledge to attempt to deal with zero-days. Often such insight is provided by a human expert hand-crafting and pre-categorising certain features as malicious. However, tightly coupled feature-engineering based on previous domain knowledge risks not being effective when faced with a new threat. In this work we decouple this human expertise, instead encapsulating knowledge inside a deep learning neural net with no prior understanding of malicious characteristics. Raw input features consist of low-level opcodes, app permissions and proprietary Android API package usage. Our method makes three main contributions. Firstly, a novel multi-view deep learning Android malware detector with no specialist malware domain insight used to select, rank or hand-craft input features. Secondly, a comprehensive zero-day scenario evaluation using the Drebin and AMD benchmarks, with our model achieving weighted average detection rates of 91% and 81% respectively, an improvement of up to 57% over the state-of-the-art. Thirdly, a 77% reduction in false positives on average compared to the state-of-the-art, with excellent F1 scores of 0.9928 and 0.9963 for the general detection task again on the Drebin and AMD benchmark datasets respectively.
Original languageEnglish
JournalJournal of Information Security and Applications
Early online date13 Jan 2021
Publication statusEarly online date - 13 Jan 2021


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