Random Neural Network for Lightweight Attack Detection in the IoT

TitleRandom Neural Network for Lightweight Attack Detection in the IoT
Publication TypeConference Paper
Year of Publication2021
AuthorsFilus K, Domańska J, Gelenbe E
Conference NameMASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
PublisherSpringer International Publishing
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.

URLhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_5
DOI10.1007/978-3-030-68110-4_5

PDF version: