@inproceedings{14d51463-6eab-492f-a927-a899368a7dbd,
  abstract     = {{<p>Industrial Control Systems (ICS) have been compromised by cyber-attacks with the development of computer and network technologies over the past few decades. Intrusion Detection Systems (IDSs) play a significant role in ensuring information security, and the key mechanism is to identify various attacks in the network accurately. One common strategy when implementing an IDS is to use a machine-learning based method. In this paper, we compare five different well-known Recurrent Neural Network (RNN) models to identify the most effective approach for use in an IDS. We evaluate and compare the models using the 'ICSFlow' dataset, based on a simulation of a bottle-filling factory. Our results show that the Multi-layered Bidirectional Gated Recurrent Unit (MBI-GRU) model outperforms other models in both accuracy and training efficiency.</p>}},
  author       = {{Zamanian, Sahar and Kihl, Maria}},
  booktitle    = {{30th IEEE Symposium on Computers and Communications, ISCC 2025}},
  issn         = {{1530-1346}},
  keywords     = {{Cyber security; Deep learning; Industrial control system; Intrusion detection System; Network attack; Network flow}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings - IEEE Symposium on Computers and Communications}},
  title        = {{An evaluation of deep learning-based models for intrusion detection in industrial control systems}},
  url          = {{http://dx.doi.org/10.1109/ISCC65549.2025.11325968}},
  doi          = {{10.1109/ISCC65549.2025.11325968}},
  year         = {{2025}},
}