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.
BT - MASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems DO - https://doi.org/10.1007/978-3-030-68110-4_5 LA - eng N2 -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.
PB - Springer International Publishing PY - 2021 SP - 79 EP - 91 T2 - MASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems T3 - Computer Communication Networks and Telecommunications, LNCS TI - Random Neural Network for Lightweight Attack Detection in the IoT UR - https://link.springer.com/chapter/10.1007/978-3-030-68110-4_5 VL - 12527 SN - 978-3-030-68110-4 ER -