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Random Neural Network for Lightweight Attack Detection in the IoT

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Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2020)

Abstract

Cyber-attack detection has become a basic component of all information processing systems, and once an attack is detected it may be possible to block or mitigate its effects. This paper addresses the use of a learning recurrent Random Neural Network (RNN) to build a lightweight detector for certain types of Botnet attacks on IoT systems. Its low computational cost based on a small 12-neuron recurrent architecture makes it particularly attractive for edge devices. The RNN can be trained off-line using a fast simplified gradient descent algorithm, and we show that it can lead to high detection rates of the order of 96%, with false alarm rates of a few percent.

This research was supported by the EU H2020 IoTAC Research and Innovation Action, funded by the European Commission (EC) under Grant Agreement ID: 952684. The EC’s support does not constitute an endorsement of this paper, which reflects the views only of the authors.

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Correspondence to Katarzyna Filus .

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Filus, K., Domańska, J., Gelenbe, E. (2021). Random Neural Network for Lightweight Attack Detection in the IoT. In: Calzarossa, M.C., Gelenbe, E., Grochla, K., Lent, R., Czachórski, T. (eds) Modelling, Analysis, and Simulation of Computer and Telecommunication Systems. MASCOTS 2020. Lecture Notes in Computer Science(), vol 12527. Springer, Cham. https://doi.org/10.1007/978-3-030-68110-4_5

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