GLASS: A Graph Learning Approach for Software Defined Network Based Smart Grid DDoS Security

Abstract

In recent years, smart grid communications (SGC) has evolved to use new technologies not only for data delivery but also for enhanced smart grid (SG) security and reliability. Software Defined Networks (SDN) has proved to be a reliable and efficient architecture for handling diverse communication systems due to their ability to divide responsibilities of the network using control plane and data plane. This paper presents a graph learning approach for detecting and identifying Distributed Denial of Service (DDoS) attacks in SDN-SGC systems (GLASS). GLASS is a two phase framework that (1) detects if SDN-SGC is under DDoS attack using supervised graph deep learning and then (2) identifies the compromised entities using unsupervised learning methods. Network performance statistics are used for modeling SDN-SGC graphs, which train Graph Convolutional Neural Networks (GCN) to extract latent representations caused by DDoS attacks. Finally, spectral clustering is used to identify compromised entities. The experimental results, obtained by analysis of an IEEE 118-bus system, show the average throughput for compromised entities is able to maintain 84% of normal traffic level with GLASS, compared to achieving only 4% of normal throughput caused by DDoS attacks on compromised entities without the GLASS framework.