DDoS Cyber-Incident Detection in Smart Grids

Jorge C. Merlino, Mohammed Asiri and Neetesh Saxena
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Jorge C. Merlino: School of Computer Science & Informatics, Cardiff University, Cardiff CF10 3AT, UK
Mohammed Asiri: School of Computer Science & Informatics, Cardiff University, Cardiff CF10 3AT, UK
Neetesh Saxena: School of Computer Science & Informatics, Cardiff University, Cardiff CF10 3AT, UK

Sustainability, 2022, vol. 14, issue 5, 1-18

Abstract: The smart grid (SG) offers potential benefits for utilities, electric generators, and customers alike. However, the prevalence of cyber-attacks targeting the SG emphasizes its dark side. In particular, distributed denial-of-service (DDoS) attacks can affect the communication of different devices, interrupting the SG’s operation. This could have profound implications for the power system, including area blackouts. The problem is that few operational technology tools provide reflective DDoS protection. Furthermore, such tools often fail to classify the types of attacks that have occurred. Defensive capabilities are necessary to identify the footprints of attacks in a timely manner, as they occur, and to make these systems sustainable for delivery of the services as expected. To meet this need for defensive capabilities, we developed a situational awareness tool to detect system compromise by monitoring the indicators of compromise (IOCs) of amplification DDoS attacks. We achieved this aim by finding IOCs and exploring attack footprints to understand the nature of such attacks and their cyber behavior. Finally, an evaluation of our approach against a real dataset of DDoS attack instances indicated that our tool can distinguish and detect different types of amplification DDoS attacks.

Keywords: IOC; industrial control systems; DDoS; situational awareness; smart grid (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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