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A Distributed Online Learning Approach to Detect Anomalies for Virtualized Network Slicing

As the network slicing is one of the critical enablers in communication networks, one anomalous physical node (PN) in substrate networks that carries multiple virtual network elements can cause significant performance degradation of multiple network slices. To recover the substrate networks from anomaly within a short time, rapid and accurate identification of whether or not the anomaly exists in PNs is vital. Online anomaly detection methods that can analyze system data in real-time are preferred. Besides, as virtual nodes mapped to PNs are scattered in multiple slices, the distributed detection modes are required to preserve the data privacy of different slices. According to those requirements, we propose a distributed online PN anomaly detection algorithm based on a decentralized one-class support vector machine (OCSVM), which is realized through analyzing real-time measurements of virtual nodes mapped to PNs in a distributed manner. Specifically, to decouple the OCSVM objective function, we transform the original problem to a group of decentralized quadratic programming problems by introducing the consensus constraints. The alternating direction method of multipliers is adopted to achieve the solution for the distributed online PN anomaly detection. The simulation results on the real-world network dataset show the effectiveness and superiority of the proposed distributed online anomaly detection algorithm.