relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3987/
title: IoT Malware Classification Based on System Calls
creator: Hoang, Dang Kien
creator: Nguyen, Dai Tho
creator: Vu, Duy Loi
subject: Information Technology (IT)
description: 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
type: Conference or Workshop Item
type: NonPeerReviewed
identifier:   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)