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

TL;DR

This paper introduces Adaptive Meta-Aggregation Federated Learning (AMAFed), a novel approach that improves intrusion detection in heterogeneous IoT networks by dynamically weighting local models through meta-learning, achieving high accuracy across multiple datasets.

Contribution

The paper presents a new federated learning method with adaptive weighting for IoT intrusion detection, addressing device heterogeneity and data quality issues.

Findings

Achieves up to 99.8% detection accuracy on ToN-IoT

F1-scores exceed 98% across datasets

Outperforms existing state-of-the-art methods

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.