An approach to detect IoT botnet attacks based on hierarchical clustering

Xiao Dong Li; Yuan Feng Song; Yu Qing Li

doi:10.1117/12.2671154

28 April 2023 An approach to detect IoT botnet attacks based on hierarchical clustering

Xiao Dong Li, Yuan Feng Song, Yu Qing Li

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126103H (2023) https://doi.org/10.1117/12.2671154
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

With the rise and development of Internet of Things (IoT) technology, more and more devices access the network. However, most of them usually ignore the security issues, the crisis such as large-scale DDoS attacks caused by IoT botnet becomes more and more severe. It is significant to study the behavior of botnet and the detection technology. In order to improve the detection performance of IoT botnet, we analyze the behavior of botnet based on the traffic in IoT environment and propose a detection approach based on hierarchical clustering. Firstly, we capture the network traffic as .pcap files and aggregate packets into data flows based on five-tuple, then extract the basic statistics features by using a time window. Secondly, we analyze the typical features of IoT botnet during waiting period and malicious active period and optimize them by hierarchical clustering. Finally, XGBoost algorithm is used to classify the botnet. To demonstrate the effectiveness of the proposed approach, we trained KNN, Decision Tree, Random Forest models over the same datasets to detect IoT botnet and compared their performance with our approach. The experimental results prove our method can efficiently detect botnet as compared to other trained models.

Citation Download Citation

Xiao Dong Li, Yuan Feng Song, and Yu Qing Li "An approach to detect IoT botnet attacks based on hierarchical clustering", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126103H (28 April 2023); https://doi.org/10.1117/12.2671154

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