eprintid: 3205
rev_number: 13
eprint_status: archive
userid: 396
dir: disk0/00/00/32/05
datestamp: 2018-12-12 06:36:08
lastmod: 2018-12-12 06:36:08
status_changed: 2018-12-12 06:36:08
type: conference_item
metadata_visibility: show
creators_name: Tran, Tuan
creators_name: Khanh, Nguyen
creators_name: Le, Son
creators_name: Phan, Anh
creators_name: Mizuhito, Ogawa
creators_name: Nguyen, Minh
creators_id: tranminhtuan@vnu.edu.vn
creators_id: mizuhito@jaist.ac.jp
creators_id: nguyenml@jaist.ac.jp
title: Comparison of Three Deep Learning-based Approaches for IoT Malware Detection
ispublished: pub
subjects: IT
divisions: fac_fit
keywords: malware detection; malware analysis; deep learning; convolutional neural network
abstract: 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
date_type: published
full_text_status: public
pres_type: paper
place_of_pub: Ho Chi Minh City, Vietnam
event_title: 2018 10th International Conference on Knowledge and Systems Engineering (KSE) (KSE'18)
event_type: conference
refereed: TRUE
citation:   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).     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3205/1/1570474504.pdf