- Title
- Malytics: a malware detection scheme
- Creator
- Yousefi-Azar, Mahmood; Hamey, Leonard G. C.; Varadharajan, Vijay; Chen, Shiping
- Relation
- IEEE Access Vol. 6, p. 49418-49431
- Publisher Link
- http://dx.doi.org/10.1109/ACCESS.2018.2864871
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2018
- Description
- An important problem of cyber-security is malware analysis. Besides good precision and recognition rate, ideally, a malware detection scheme needs to be able to generalize well for novel malware families (a.k.a zero-day attacks). It is important that the system does not require excessive computation particularly for deployment on the mobile devices. In this paper, we propose a novel scheme to detect malware which we call Malytics. It is not dependent on any particular tool or operating system. It extracts static features of any given binary file to distinguish malware from benign. Malytics consists of three stages: feature extraction, similarity measurement and classification. The three phases are implemented by a neural network with two hidden layers and an output layer. We show feature extraction, which is performed by tf-simhashing, is equivalent to the first layer of a particular neural network. We evaluate Malytics performance on both Android and Windows platforms. Malytics outperforms a wide range of learning-based techniques and also individual state-of-the-art models on both platforms. We also show Malytics is resilient and robust in addressing zero-day malware samples. The F1-score of Malytics is 97:21% and 99:45% on Android dex file and Windows PE files respectively, in the applied datasets. The speed and efficiency of Malytics are also evaluated.
- Subject
- malware detection; static analysis; binary level n-grams; term frequency simhashing
- Identifier
- http://hdl.handle.net/1959.13/1394102
- Identifier
- uon:33650
- Identifier
- ISSN:2169-3536
- Rights
- © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission
- Language
- eng
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT02 | Publisher version (open access) | 7 MB | Adobe Acrobat PDF | View Details Download |