Federated transfer learning for rare attack class detection in network intrusion detection systems.

Federated learning (FL) offers a promising approach for training machine learning models with minimal data sharing, enhancing privacy and performance. However, building effective FL-based network intrusion detection systems (NIDS) remains challenging due to the need for large, diverse training datasets. Identifying rare attack types with limited instances is a persistent obstacle, and their detection is critical in cybersecurity. This research introduces a novel FL framework to address these challenges. By incorporating adaptive, personalized layers at the client level, the model reduces false alarm rates for zero-day attack types and improves the detection of rare classes. The model also leverages Transfer Learning (TL) to identify zero-day attacks, where client-specific gradients are collected and used to update a global model on the server side after multiple rounds of exposure to new data. The proposed sustainable framework aims to disseminate knowledge about rare attack types across clients through a server-based global model within the FL ecosystem. This study achieves two main objectives: (i) improving the detection of rare attack classes and (ii) identifying zero-day attacks in a NIDS context. Evaluations on the CSE-CICIDS-2018, Edge IIoT, and UNSW-NB 15 datasets, which encompass diverse class distributions, demonstrate that the proposed approach outperforms existing models in detecting and handling rare and novel attack types. The proposed model achieves 98.90% accuracy on CICIDS 2018, 98.70% on UNSW-NB 15, and 97.92% on Edge-IIoT, surpassing the FL-TL-CNN model by 2.78%, 1.51%, and 2.03%, respectively. These results highlight the effectiveness, robustness, and adaptability of the proposed approach in enhancing intrusion detection across heterogeneous network environments.