The ubiquitous nature of the Internet of Things (IoT) network and inherent vulnerability characteristics make them a prime target for highly crafted and sophisticated threats. Although traditional centralized intrusion detection systems (IDS) have shown effectiveness in conventional network environments, they often struggle to address the unique challenges presented by the scale, heterogeneity, and resource constraints inherent in IoT. This research addresses the critical need for lightweight and computationally efficient security solutions by proposing a novel lightweight IDS method on decentralized data LightDFK. LightDFK integrates the synergistic combination of Federated Learning (FL) and Knowledge Distillation (KD) to monitor thetraffic of IoT devices for malicious activity detection. Using entropy-based weight aggregation, client selection, and logits transfer, LightDFK learns a global model between 10 and 30 rounds of communication. LightDFK effectively addresses the challenges of non-independently and identically distributed (non-IID) data in heterogeneous IoT environments by leveraging client-weighted contributions and diverse model architectures to enhance robustness and performance. The experimental validation of this method utilizes three distinct and relevant datasets: ACIIoT2023, FLNET2023, and CICIDS2017. The results demonstrate a lightweight, high-performance IDS model capable of accurately detecting intrusions in IoT environments, achieving 37. 34% higher accuracy compared to FedAvg on FLNET2023 and CICIDS2017 at = 0. 5 and a 93% reduction in model complexity.
Okey et al. (Thu,) studied this question.