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.

翻译：物联网（IoT）的快速普及通过实现互联系统和智能自动化，为各行业带来了显著进步。然而，这种指数级增长也引入了严重的安全漏洞，使得物联网网络日益成为复杂网络攻击的目标。物联网设备的异构性对传统入侵检测系统构成了严峻挑战。为应对这些挑战，本文提出了一种名为自适应元聚合联邦学习（AMAFed）的创新方法，旨在增强异构物联网网络中的入侵检测能力。该方法通过利用元学习的动态加权机制，根据本地模型的数据质量和贡献度分配自适应权重，从而实现跨设备的个性化协同学习。所提方法在三个基准物联网数据集上进行了评估：ToN-IoT、N-BaIoT和BoT-IoT，这些数据集代表了多样化的真实场景。实验结果表明，AMAFed在ToN-IoT数据集上实现了高达99.8%的检测准确率，在所有数据集上的F1分数均超过98%。在N-BaIoT数据集上达到99.88%的准确率，在BoT-IoT数据集上达到98.12%的准确率，其性能持续优于现有先进方法。