Lightweight Distributed Cloud-Native Service Function Chain Anomaly Detection for Edge-Cloud Networks

Anomaly detection in Service Function Chains (SFCs) is essential for ensuring the security of edge-cloud networks. However, edge servers in Industrial Internet of Things (IIoT) face challenges in meeting the real-time processing requirements for high-precision anomaly detection due to limited computational resources. In order to address this issue, we initially propose an architectural framework for in-band measurement of cloud-native SFC, which can efficiently detect the state information of virtual network functions (VNFs). Secondly, we propose LightSFC, a lightweight distributed edge-cloud network service function chain anomaly detection model. LightSFC achieves comprehensive awareness of the SFC state by collecting multi-source information from both the data plane and the control plane, and utilizes a lightweight deep Autoencoder model for proactive anomaly detection. Our experimental results show that LightSFC is capable of rapidly detecting anomalies with lower resource overhead. Compared to other methods, LightSFC exhibits superior performance in terms of accuracy, precision, recall, and F1-score, thereby substantiating its efficacy in SFC anomaly detection for edge-cloud networks.