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<p>Recent advancements in Internet of Things (IoT) networks have introduced significant security challenges, particularly in detecting and preventing cyber-attacks while preserving data privacy. This paper presents a novel Hybrid Federated Ensemble Learning (HFEL) framework that combines the privacy-preserving benefits of federated learning with the robust detection capabilities of ensemble methods. Our approach integrates three complementary models— Multi-Layer Perceptron, Random Forest, and XGBoost—in a federated learning environment, utilizing majority voting for final decision-making. The framework is extensively evaluated on two benchmark datasets: BoT-IoT and Edge-IIoT. Our experimental results demonstrate that HFEL significantly outperforms traditional federated learning approaches, achieving 99.94% accuracy on the BoT-IoT dataset and 97.53% on the Edge-IIoT dataset. Notably, HFEL maintains consistent performance from initial deployment, addressing the cold-start problem common in federated learning systems. Comparative analysis with state-of-the-art methods confirms HFEL’s superior detection capabilities while preserving data privacy, making it particularly suitable for securing distributed IoT networks.</p>

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HFEL: A hybrid federated ensemble learning framework for intrusion detection in IoT networks

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Salah El Hajla,
  • El Mahfoud Ennaji,
  • Yassine Maleh,
  • Soufyane Mounir

摘要

Recent advancements in Internet of Things (IoT) networks have introduced significant security challenges, particularly in detecting and preventing cyber-attacks while preserving data privacy. This paper presents a novel Hybrid Federated Ensemble Learning (HFEL) framework that combines the privacy-preserving benefits of federated learning with the robust detection capabilities of ensemble methods. Our approach integrates three complementary models— Multi-Layer Perceptron, Random Forest, and XGBoost—in a federated learning environment, utilizing majority voting for final decision-making. The framework is extensively evaluated on two benchmark datasets: BoT-IoT and Edge-IIoT. Our experimental results demonstrate that HFEL significantly outperforms traditional federated learning approaches, achieving 99.94% accuracy on the BoT-IoT dataset and 97.53% on the Edge-IIoT dataset. Notably, HFEL maintains consistent performance from initial deployment, addressing the cold-start problem common in federated learning systems. Comparative analysis with state-of-the-art methods confirms HFEL’s superior detection capabilities while preserving data privacy, making it particularly suitable for securing distributed IoT networks.