A Novel Federated Learning Based Intrusion Detection System for IoT Networks

In the realm of IoT platforms, susceptibility to cyber-attacks is a pressing concern, necessitating the deployment of Intrusion Detection Systems (IDS). Constructing a scalable, accurate, and lightweight model without compromising data privacy poses a formidable challenge. This study assesses classical and novel approaches employing federated learning (FL) to train IDS models. Optimization through Knowledge Distillation (KD) techniques aims to enhance computational efficiency. Experimental results reveal the efficacy of federated learning, achieving an 84.5% accuracy for 15 attack types, and an impressive performance for binary network attack classification. Notably, these models exhibit shorter inference times compared to cutting-edge machine learning models trained on the Edge-IIoTset dataset, offering promising advancements in IoT security.