Federated Intrusion Detection on Non-IID Data for IIoT networks using Generative Adversarial Networks and Reinforcement Learning

RESEARCH CREW
8:05 29/08/2022

Federated learning 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.

TIN LIÊN QUAN
The abuse of prescription medications has become a severe public health crisis fueled by limited coordination and oversight across healthcare systems. Current frameworks lack interoperability between doctors, pharmacies, and regulators, enabling abusive practices like doctor shopping and pharmacy hopping. To address these issues, this research proposes Medichain, a novel multichain-based...