Toward Resilient Smart Grid Communications Using Distributed SDN with ML-Based Anomaly Detection

Part 2: Learning-Based NetworkingInternational audienceNext generation “Smart” systems, including cyber-physical systems like smart grid and Internet-of-Things, integrate control, communication and computation to achieve stability, efficiency and robustness of physical processes. While a great amount of research has gone towards building these systems, security in the form of resilient and fault-tolerant communications for smart grid systems is still immature. In this paper, we propose a hybrid, distributed and decentralized (HDD) SDN architecture for resilient Smart Systems. It provides a redundant controller design for fault-tolerance and fail-over operation, as well as parallel execution of multiple anomaly detection algorithms. Using the k-means clustering algorithm from the machine learning literature, it is shown that k-means can be used to produce a high accuracy (96.9%) of identifying anomalies within normal traffic. Furthermore, incremental k-means produces a slightly lower accuracy (95.6%) but demonstrated an increased speed with respect to k-means and fewer CPU and memory resources needed, indicating a possibility for scaling the system to much larger networks