Temporal Partitioned Federated Learning for IoT Intrusion Detection Systems

Machine learning-based intrusion detection systems (IDSs) serve as a defense-in-depth layer for Internet of Things (IoT) networks by detecting potential intrusions within IoT traffic. However, resource-constrained IoT devices impose significant challenges in developing effective IDSs. Recently, federated learning (FL) has emerged as a promising solution for training detection models on distributed IoT devices without compromising resource limitations resulting in the introduction of FL-based IDSs. To this end, this paper introduces a novel approach to enhance the effectiveness of current FL-based IoT IDSs while utilizing the same resources. The proposed system partitions the FL rounds between IoT device groups, allowing each group to update the FL detection model during its time partition. Hence, by implementing this temporal partitioning, multiple detection models are updated within one FL round using the same IoT resources. The main design goals of the proposed approach are to improve detection accuracy and convergence time compared to the traditional approach. For this purpose, the proposed approach is evaluated and compared to the traditional approach using five intrusion scenarios on IoT traffic obtained from the Edge-IIoTset dataset. The results demonstrate that the proposed temporal partitioned FL-based IoT IDS outperforms the traditional system by achieving higher detection accuracy and faster convergence time. Furthermore, the proposed approach achieves the required detection accuracy in fewer FL rounds, which can, in principle, save more IoT resources.