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While the effectiveness of Machine Learning (ML)-enabled Intrusion Detection Systems (IDSs) inherently depends on the quality of the training dataset, privacy constraints often impede the share of such data. As a result, the emergence of Federated Learning (FL) has attracted significant attention due to its capacity to ensure data privacy by obviating the need for data sharing. In this paper, the authors explore the integration of FL for intrusion detection, delving into its potential benefits and challenges. This investigation revealed that while FL-based IDSs offer numerous advantages over traditional approaches, ensuring the privacy and security of both local and global updates and establishing trust between participants, are critical challenges that must be addressed to ensure the successful deployment of FL-based IDS solutions in real-world environments.

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Federated Learning for Intrusion Detection: Opportunities and Challenges

  • 会议论文

  • 发表日期：

    2025年

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  • Oumaima Chakir,
  • Yassine Sadqi

摘要

While the effectiveness of Machine Learning (ML)-enabled Intrusion Detection Systems (IDSs) inherently depends on the quality of the training dataset, privacy constraints often impede the share of such data. As a result, the emergence of Federated Learning (FL) has attracted significant attention due to its capacity to ensure data privacy by obviating the need for data sharing. In this paper, the authors explore the integration of FL for intrusion detection, delving into its potential benefits and challenges. This investigation revealed that while FL-based IDSs offer numerous advantages over traditional approaches, ensuring the privacy and security of both local and global updates and establishing trust between participants, are critical challenges that must be addressed to ensure the successful deployment of FL-based IDS solutions in real-world environments.