Abstract
A new memory structure for artificial neural networks of adaptive-resonance theory is proposed, which has a hierarchical form. For each new memory level, the previous classification value is refined by increasing the similarity parameter. This architecture was used to determine the network state in intrusion detection systems. The paper describes an algorithm for learning the proposed structure of the ART-2m network in parallel mode. A comparative analysis of the time characteristics of the network with the proposed structure when operating in series and parallel modes is carried out. Experiments were carried out using the NSL KDD-2009 sample, the results of which show the possibility of using ART-2m in intrusion detection systems. Most states were determined with sufficient accuracy that is greater than 80%.
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Acknowledgment
This work was supported by Russian Foundation for Basic Research, project 19-29-09056 mk and Ministry of Education of Russia (grant IS) project No. 13.
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Polyakov, V., Bukhanov, D., Panchenko, M. (2021). Identification of the Network State Based on the ART-2 Neural Network with a Hierarchical Memory Structure in Parallel Mode. In: Kovalev, S.M., Kuznetsov, S.O., Panov, A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science(), vol 12948. Springer, Cham. https://doi.org/10.1007/978-3-030-86855-0_3
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DOI: https://doi.org/10.1007/978-3-030-86855-0_3
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