Federated learning (FL) has become the promising approach for building collaborative intrusion detection systems (IDS) as providing privacy guaranteeing among data holders. Nevertheless, the non-independent and identically distributed (Non-IID) data in real-world scenarios negatively impacts the performance of aggregated models from training client updates. To this end, in this paper, we introduce Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) approach for federated IDS that can deal with Non-IID data among organizational networks. More specifically, the imbalanced state between data classes is tackled by GAN-based data augmentation, while RL provides better performance in the client choosing process for federated IDS model training. Finally, the experimental results on Kitsune dataset indicate that our work can help to set up the collaboration between data holders for building more effective IDS to deploy in practice with distinguished data distribution.
CITATION STYLE
Quyen, N. H., Duy, P. T., Vy, N. C., Hien, D. T. T., & Pham, V. H. (2022). Federated Intrusion Detection on Non-IID Data for IIoT Networks Using Generative Adversarial Networks and Reinforcement Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13620 LNCS, pp. 364–381). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21280-2_20
Mendeley helps you to discover research relevant for your work.