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The rapid expansion of Internet of Things (IoT) devices has significantly increased the attack surface, necessitating the development of robust and privacy-preserving intrusion detection systems. This research delves into the application of Federated Learning (FL) to detect network-based attacks in IoT environments, utilizing the NF-ToN-IoT dataset. We compare a centralized machine learning model with a federated counterpart, employing the Federated Averaging (FedAvg) algorithm. Both models employ a Multi-Layer Perceptron (MLP) architecture trained on NetFlow features for binary classification of benign versus malicious traffic. Our experimental results reveal that the federated model achieves comparable and even slightly superior performance across various metrics, including precision, recall, and F1-score, while preserving data privacy by decentralizing the training data. These findings underscore the potential of FL as a viable alternative to traditional intrusion detection systems in real-world, privacy-sensitive IoT scenarios.

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Federated Learning for the Detection of Attacks on IoT Environment

  • 会议论文

  • 发表日期：

    2026年

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  • Alejandro Diez,
  • Jaime Rincon,
  • Daniel Urda

摘要

The rapid expansion of Internet of Things (IoT) devices has significantly increased the attack surface, necessitating the development of robust and privacy-preserving intrusion detection systems. This research delves into the application of Federated Learning (FL) to detect network-based attacks in IoT environments, utilizing the NF-ToN-IoT dataset. We compare a centralized machine learning model with a federated counterpart, employing the Federated Averaging (FedAvg) algorithm. Both models employ a Multi-Layer Perceptron (MLP) architecture trained on NetFlow features for binary classification of benign versus malicious traffic. Our experimental results reveal that the federated model achieves comparable and even slightly superior performance across various metrics, including precision, recall, and F1-score, while preserving data privacy by decentralizing the training data. These findings underscore the potential of FL as a viable alternative to traditional intrusion detection systems in real-world, privacy-sensitive IoT scenarios.