Data-Driven Edge Intelligence for Robust Network Anomaly Detection

The advancement of networking platforms for assured online services requires robust and effective network intelligence systems against anomalous events and malicious threats. With the rapid development of modern communication technologies, artificial intelligence, and the revolution of computing devices, cloud computing empowered network intelligence will inevitably become a core platform for various smart applications. While cloud computing provides strong and powerful computation, storage, and networking services to detect and defend cyber threats, edge computing on the other hand will deliver more benefits in specific yet potential critical areas. In this paper, we present a study on the data-driven edge intelligence for robust network anomaly detection. We first highlight the main motivations for edge intelligence, and then propose an intelligence system empowered by edge computing for network anomaly detection. We further propose a scheme on the data-driven robust network anomaly detection. In the proposed scheme, four phases are designed to incorporate with data-driven approaches to train a learning model which is able to detect and identify a network anomaly in a robust way. In the performance evaluations with data experiments, we demonstrate that the proposed scheme achieves the robustness of trained model and the efficiency on the detection of specific anomalies.