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<p>Intrusion detection in Fog-enabled Internet of Things (IoT) environments presents unique challenges due to the distributed and heterogeneous nature of data sources. Traditional centralized approaches may not be suitable for Fog computing, where data privacy and latency constraints are critical. This paper proposes a novel framework that integrates federated learning (FL) and generative adversarial networks (GANs) for intrusion detection in Fog-enabled IoT networks. In our approach, each Fog node trains a local GAN model using FL, where the GAN’s discriminator learns to distinguish between normal and anomalous data patterns specific to its local environment. The federated aggregation of these local models at a central server enhances the global understanding of intrusion behaviors across the Fog network without compromising data privacy. We present detailed algorithms for local GAN training, federated model aggregation, and real-time intrusion detection using the GAN discriminator. Experimental results demonstrate the effectiveness of our approach in detecting various types of intrusions while maintaining low latency and preserving data confidentiality in Fog environments.</p>

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Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Ting Lei

摘要

Intrusion detection in Fog-enabled Internet of Things (IoT) environments presents unique challenges due to the distributed and heterogeneous nature of data sources. Traditional centralized approaches may not be suitable for Fog computing, where data privacy and latency constraints are critical. This paper proposes a novel framework that integrates federated learning (FL) and generative adversarial networks (GANs) for intrusion detection in Fog-enabled IoT networks. In our approach, each Fog node trains a local GAN model using FL, where the GAN’s discriminator learns to distinguish between normal and anomalous data patterns specific to its local environment. The federated aggregation of these local models at a central server enhances the global understanding of intrusion behaviors across the Fog network without compromising data privacy. We present detailed algorithms for local GAN training, federated model aggregation, and real-time intrusion detection using the GAN discriminator. Experimental results demonstrate the effectiveness of our approach in detecting various types of intrusions while maintaining low latency and preserving data confidentiality in Fog environments.