Nguyen, K.D.T. and Tuan, T.M. and Le, S.H. and Viet, A.P. and Ogawa, M. and Minh, N.L. (2018) Comparison of Three Deep Learning-based Approaches for IoT Malware Detection. In: 10th International Conference on Knowledge and Systems Engineering, KSE 2018, 1 November 2018 through 3 November 2018.
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The development of IoT brings many opportunities but also many challenges. Recently, increasingly more malware has appeared to target IoT devices. Machine learning is one of the typical techniques used in the detection of malware. In this paper, we survey three approaches for IoT malware detection based on the application of convolutional neural networks on different data representations including sequences, images, and assembly code. The comparison was conducted on the task of distinguishing malware from nonmalware. We also analyze the results to assess the pros/cons of each method. © 2018 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Divisions: | Faculties > Faculty of Information Technology Faculties > Faculty of Special Equipments |
| Identification Number: | 10.1109/KSE.2018.8573374 |
| Uncontrolled Keywords: | Computer crime; Deep learning; Malware; Neural networks; Systems engineering; Assembly code; Convolutional neural network; Data representations; Iot devices; Learning-based approach; Malware detection; Internet of things |
| Additional Information: | Conference code: 143626. Language of original document: English. |
| URI: | http://eprints.lqdtu.edu.vn/id/eprint/9488 |
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