@inproceedings{bibcite_15559,
  author = {Katarzyna Filus and Joanna Doma{\'n}ska and Erol Gelenbe},
  title = {Random Neural Network for Lightweight Attack Detection in the IoT},
  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>
},
  year = {2021},
  journal = {MASCOTS 2020: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems},
  volume = {12527},
  pages = {79-91},
  publisher = {Springer International Publishing},
  issn = {978-3-030-68110-4},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-68110-4_5},
  doi = {https://doi.org/10.1007/978-3-030-68110-4_5},
  language = {eng},
}