Privacy-Preserving Collaborative Intrusion Detection in Edge of Internet of Things: A Robust and Efficient Deep Generative Learning Approach.

The swift expansion of the Internet of Things (IoT) has brought about convenient services, but it has also increased cyber threats. Intrusion Detection Systems (IDS) is an effective tools of mitigating security concerns by identifying suspicious network activities. Many decentralized deep learning methods such as federated learning have been applied for intrusion detection. However, developing an effective and reliable collaborative IDS is still challenging due to data privacy leakage during model updates and high communication overhead by local model parameters. Moreover, existing federated learning methods are limited in practicality since they only perform well under data independent identically distribution (IID), which is not commonly found in real scenarios. To solve these issues, we propose a novel collaborative intrusion detection framework with strong privacy preservation in IoT networks (CIDIoT). Specifically, the CIDIoT extends the improved generative adversarial network model without exchanging individual network data to enable efficient intrusion detection. To enhance the privacy-preserving of the framework, differential privacy noise and dynamic threshold secret sharing are added to the uploaded model information and downloaded data while keeping communication efficiency. A novel robust aggregation method is also developed to increase the robustness of the CIDIoT against imbalanced and Non-IID data case. Extensive experimental results on two real-world heterogeneous datasets validate that CIDIoT significantly outperforms other state-of-the-art methods in terms of detection accuracy, communication overhead, and cooperative privacy preservation.