Flow-Based IoT Intrusion Detection via Improved Generative Federated Distillation Learning

Zeyu Li, Wenbin Yao, Juanjuan Luo, Zhibin Huang

Abstract: With the rapid development of information technology, cybersecurity issues are becoming increasingly prominent. Existing intrusion detection methods are mainly based on centralized machine learning algorithms, which overlook the data privacy issues of edge devices on the Internet of Things (IoT). Therefore, federated learning has been proposed by researchers to address the problems of data privacy and leakage in intrusion detection. However, existing intrusion detection algorithms based on federated learning face the following problems: 1) lack of a standard network traffic feature set; 2) clients exhibit data heterogeneity, which severely impacts the training of federated learning; and 3) knowledge-distillation-based federated learning requires the server to possess a proxy dataset, which may be impractical in certain situations. To address these issues, this article proposes an intrusion detection model based on improved generative federated distillation learning (FedGen+). Specifically, we use the original network traffic format as the input to the clients. The server learns a generator to integrate the clients’ label information, then the generator is deployed to clients to generate augmented sample feature representations to train local model. Experimental results demonstrate that the FedGen+ outperforms existing federated-learning-based intrusion detection algorithms on the IoT benchmark datasets.