Random Neural Network for Lightweight Attack Detection in the IoT
| Author | Filus K.; Domańska J.; Gelenbe E. |
|---|---|
| Title | Random Neural Network for Lightweight Attack Detection in the IoT |
| Journal | MASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems |
| Year | 2021 |
| Status | Published |
| Volume | 12527 |
| Pages | 79-91 |
| DOI | https://doi.org/10.1007/978-3-030-68110-4_5 |
| URL | https://link.springer.com/chapter/10.1007/978-3-030-68110-4_5 |
| Abstract | <p>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.</p> |
| Publisher | Springer International Publishing |
| ISSN | 978-3-030-68110-4 |
| Random Neural Network for Lightweight Attack Detection in the IoT.pdf |