01166nas a2200169   4500000000100000008004100001260003800042100002000080700002100100700001700121245007000138856006600208300001000274490001000284520068000294022002200974       2021                            d  bSpringer International Publishing1 aKatarzyna Filus1 aJoanna Domańska1 aErol Gelenbe00aRandom Neural Network for Lightweight Attack Detection in the IoT  uhttps://link.springer.com/chapter/10.1007/978-3-030-68110-4_5  a79-910 v125273 a<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>
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