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http://dx.doi.org/10.9708/jksci.2022.27.07.101

Machine Learning-based Detection of DoS and DRDoS Attacks in IoT Networks  

Yeo, Seung-Yeon (Dept. of Information Security, Seoul Women's University)
Jo, So-Young (Dept. of Information Security, Seoul Women's University)
Kim, Jiyeon (Dept. of Computer Engineering, Daegu University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.7, 2022 , pp. 101-108 More about this Journal
Abstract
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.
Keywords
Internet of Things; Intrusion Detection; Machine Learning; Denial of Service; Distributed Reflection Denial of Service;
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Times Cited By KSCI : 4  (Citation Analysis)
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