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A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction

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Abstract

The application of multiagent reinforcement learning technology to solve the problem of intrusion detection in the Internet of Things (IoT) systems is considered. Three models of a multiagent intrusion detection system are implemented: a completely decentralized system, a system with the transfer of forecast data, and a system with the transfer of observation data. The experimental results are given in comparison with the Suricata open-code intrusion detection system. The considered architectures of multiagent systems are shown to be free from the shortcomings of the existing solutions.

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Funding

The research is funded by the Ministry of Science and Higher Education of the Russian Federation as part of the World-class Research Center program: Advanced Digital Technologies (contract no. 075-15-2022-311 dated 20.04.2022).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    M. O. Kalinin & E. I. Tkacheva

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  1. M. O. Kalinin
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  2. E. I. Tkacheva
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Correspondence to M. O. Kalinin.

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The authors of this work declare that they have no conflicts of interest.

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Translated by E. Glushachenkova

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Kalinin, M.O., Tkacheva, E.I. A Decentralized Approach to Intrusion Detection in Dynamic Networks of the Internet of Things Based on Multiagent Reinforcement Learning with Interagent Interaction. Aut. Control Comp. Sci. 57, 1025–1032 (2023). https://doi.org/10.3103/S0146411623080096

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