relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3205/
title: Comparison of Three Deep Learning-based Approaches for IoT Malware Detection
creator: Tran, Tuan
creator: Khanh, Nguyen
creator: Le, Son
creator: Phan, Anh
creator: Mizuhito, Ogawa
creator: Nguyen, Minh
subject: Information Technology (IT)
description: The development of IoT brings many opportunities but also many challenges. Recently, increasing more malware has been 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.
date: 2018-10-01
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3205/1/1570474504.pdf
identifier:   Tran, Tuan and Khanh, Nguyen and Le, Son and Phan, Anh and Mizuhito, Ogawa and Nguyen, Minh  (2018) Comparison of Three Deep Learning-based Approaches for IoT Malware Detection.  In: 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (KSE'18).