Adaptive Meta-Aggregation Federated Learning for Intrusion Detection in Heterogeneous Internet of Things

Abstract: The rapid proliferation of the Internet of Things (IoT) has brought remarkable advancements to industries by enabling interconnected systems and intelligent automation. However, this exponential growth has also introduced significant security vulnerabilities, making IoT networks increasingly targets for sophisticated cyberattacks. The heterogeneity of IoT devices poses critical challenges for traditional intrusion detection systems. To address these challenges, this paper proposes an innovative method called Adaptive Meta-Aggregation Federated Learning (AMAFed), designed to enhance intrusion detection in heterogeneous IoT networks. By employing a dynamic weighting mechanism using meta-learning, AMAFed assigns adaptive importance to local models based on their data quality and contributions, enabling personalized yet collaborative learning across devices. The proposed method was evaluated on three benchmark IoT datasets: ToN-IoT, N-BaIoT, and BoT-IoT, representing diverse real-world scenarios. Experimental results demonstrate that AMAFed achieves detection accuracy up to 99.8% on ToN-IoT, with F1-scores exceeding 98% across all datasets. On the N-BaIoT dataset, it reaches 99.88% accuracy, and on BoT-IoT, it achieves 98.12% accuracy, consistently outperforming state-of-the-art approaches.