Efficient security service function chaining based on federated learning in edge networks

Abstract

The escalating demand for network services has prompted the evolution of Service Function Chaining (SFC) within 6G networks to deliver sophisticated, customized services while ensuring robust cybersecurity. This paper introduces an efficient and secure framework for SFC in Mobile Edge Computing (MEC) environments, termed the Federated Learning-based SFC (FL-SFC), which integrates SFC, MEC, and Federated Learning (FL) to enhance service policy decision-making and safeguard user privacy. The FL-SFC framework enables dynamic updating of service policies and optimizes communication efficiency. We propose an anomaly detection model, CNN-GRU, which combines Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to significantly improve anomaly detection performance at the network edge. Additionally, to address the high communication costs associated with service policy models, we have designed a model compression mechanism leveraging sparsification and quantization techniques, which substantially reduces communication overhead during model training. Simulation experiments demonstrated the superiority of the FL-SFC framework and the CNN-GRU model in detection performance over existing methods. Results indicate that our model excels in accuracy, precision, recall, and F1-score while significantly reducing the number of communication bits, thereby validating the effectiveness of our approach.