Random Neural Network for Lightweight Attack Detection in the IoT

TytułRandom Neural Network for Lightweight Attack Detection in the IoT
Publication TypeConference Paper
Rok publikacji2021
AutorzyFilus K, Domańska J, Gelenbe E
Conference NameMASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
PublisherSpringer International Publishing

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.


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Data aktualizacji: 04/01/2022 - 10:45; autor zmian: Jarosław Miszczak (miszczak@iitis.pl)