An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks

Wang, Zhen and Zainal, Anazida and Siraj, Maheyzah Md and Ghaleb, Fuad A. and Hao, Xue and Han, Shaoyong (2025) An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

Item Type:	Article
Identification Number:	10.1038/s41598-024-85083-8
Dates:	Date Event 31 December 2024 Accepted 14 January 2025 Published Online
Uncontrolled Keywords:	Kolmogorov-Arnold Networks, Convolutional neural network, Intrusion detection, Deep learning, Artificial intelligence
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	19 Aug 2025 13:27
Last Modified:	19 Aug 2025 13:27
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16602

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