eprintid: 3987
rev_number: 20
eprint_status: archive
userid: 431
dir: disk0/00/00/39/87
datestamp: 2020-07-18 01:59:11
lastmod: 2020-07-18 01:59:11
status_changed: 2020-07-18 01:59:11
type: conference_item
metadata_visibility: show
creators_name: Hoang, Dang Kien
creators_name: Nguyen, Dai Tho
creators_name: Vu, Duy Loi
creators_id: kienhd1@vnu.edu.vn
creators_id: nguyendaitho@vnu.edu.vn
creators_id: vuduyloi55@gmail.com
title: IoT Malware Classification Based on System Calls
ispublished: inpress
subjects: IT
divisions: avitech
divisions: fac_fit
abstract: IoT devices play an important role in the industrial revolution 4.0. However, this type of device may exhibit specific security vulnerabilities that can be easily exploited to cause botnet attacks and other malicious activities. In this paper, we introduce a new method for
classification and clustering of IoT malware behaviors
through system call monitoring. Our method is constructed
from multiple one-class SVM classifiers and has the ability to classify known malware with F1-Score over 98% and probability to detect unknown malware up to 97%. Unknown malware instances with similar behaviors can also be grouped together so new classes of malware will be discovered.
date: 2020
date_type: published
full_text_status: none
pres_type: paper
event_title: The 2020 IEEE-RIVF International Conference on Computing and Communications Technologies
event_location: Ho Chi Minh
event_type: conference
refereed: FALSE
citation:   Hoang, Dang Kien and Nguyen, Dai Tho and Vu, Duy Loi  (2020) IoT Malware Classification Based on System Calls.  In: The 2020 IEEE-RIVF International Conference on Computing and Communications Technologies, Ho Chi Minh.    (In Press)