Safeguarding the Smart Home: Heterogeneous Federated Deep Learning for Intrusion Defense

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Abstract
This study introduces an advanced federated learning framework tailored for smart home intrusion detection, incorporating knowledge distillation and transfer learning to tackle escalating threats to IoT devices. In light of the rapid expansion of IoT devices and their vulnerability to botnet incursions, our approach specifically addresses the challenges related to the privacy concerns of home device data, heterogeneity of devices, sparse intrusion data, and the dynamic nature of smart home settings. We adaptively select model architectures tailored to the computational capabilities of each device, ranging from simple Neural Networks (NNs) to more complex Convolutional Neural Networks (CNNs) and hybrid CNN-LSTM models, ensuring efficient local training without overburdening the devices. However, it can achieve good performance through collaborative learning, even for devices with lower capacity and sparse data. Our evaluation, conducted using the N-BaIoT dataset, demonstrates the effectiveness of our approach in detecting anomalies across a diverse set of IoT devices infected with real-world botnets such as Mirai and BASHLITE. The results highlight the potential of our framework to provide a robust, privacy-preserving, and adaptable solution for securing smart homes against emerging threats.
Funding Information
  • National Science Foundation

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