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.
CITATION STYLE
Filus, K., Domańska, J., & Gelenbe, E. (2021). Random Neural Network for Lightweight Attack Detection in the IoT. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12527 LNCS, pp. 79–91). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68110-4_5
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