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Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
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GLASS: A Graph Learning Approach for Software Defined Network Based Smart Grid DDoS Security
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.
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- Award ID(s):
- 1809739
- PAR ID:
- 10294281
- Date Published:
- Journal Name:
- IEEE ICC 2021 - IEEE International Conference on Communications
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICC42927.2021.9500999
