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@article{ART002862989},
author={Seung-Yeon Yeo and So-Young Jo and Jiyeon Kim},
title={Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={7},
pages={101-108},
doi={10.9708/jksci.2022.27.07.101}

TY - JOUR
AU - Seung-Yeon Yeo
AU - So-Young Jo
AU - Jiyeon Kim
TI - Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 7
PB - The Korean Society Of Computer And Information
SP - 101
EP - 108
SN - 1598-849X
AB - We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.
KW - Internet of Things;Intrusion Detection;Machine Learning;Denial of Service;Distributed Reflection Denial of Service
DO - 10.9708/jksci.2022.27.07.101
ER -

Seung-Yeon Yeo, So-Young Jo and Jiyeon Kim. (2022). Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks. Journal of The Korea Society of Computer and Information, 27(7), 101-108.

Seung-Yeon Yeo, So-Young Jo and Jiyeon Kim. 2022, "Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks", Journal of The Korea Society of Computer and Information, vol.27, no.7 pp.101-108. Available from: doi:10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo, So-Young Jo, Jiyeon Kim "Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks" Journal of The Korea Society of Computer and Information 27.7 pp.101-108 (2022) : 101.

Seung-Yeon Yeo, So-Young Jo, Jiyeon Kim. Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks. 2022; 27(7), 101-108. Available from: doi:10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo, So-Young Jo and Jiyeon Kim. "Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks" Journal of The Korea Society of Computer and Information 27, no.7 (2022) : 101-108.doi: 10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo; So-Young Jo; Jiyeon Kim. Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks. Journal of The Korea Society of Computer and Information, 27(7), 101-108. doi: 10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo; So-Young Jo; Jiyeon Kim. Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks. Journal of The Korea Society of Computer and Information. 2022; 27(7) 101-108. doi: 10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo, So-Young Jo, Jiyeon Kim. Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks. 2022; 27(7), 101-108. Available from: doi:10.9708/jksci.2022.27.07.101

Seung-Yeon Yeo, So-Young Jo and Jiyeon Kim. "Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks" Journal of The Korea Society of Computer and Information 27, no.7 (2022) : 101-108.doi: 10.9708/jksci.2022.27.07.101