A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays

• Published in: IEEE transactions on smart grid Vol. 12; no. 3; pp. 2554 - 2565
• Main Authors: Khaw, Yew Meng, Abiri Jahromi, Amir, Arani, Mohammadreza F. M., Sanner, Scott, Kundur, Deepa, Kassouf, Marthe
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Control systems; Critical components; Cyberattack; cyberattack detection systems; Cyberphysical systems; Cybersecurity; Deep learning; Digitization; Electrical measurement; Fault detection; IEC Standards; Industrial electronics; operational technology; Power systems; Protective relaying; Protocols; Remote control; Substations; Supply chains; Transmission line measurements; Transmission lines; transmission protective relays; Utilities
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2020.3040361

The digitalization of power systems over the past decade has made the cybersecurity of substations a top priority for regulatory agencies and utilities. Proprietary communication protocols are being increasingly replaced by standardized and interoperable protocols providing utility operators with remote access and control capabilities at the expense of growing cyberattack risks. In particular, the potential of supply chain cyberattacks is on the rise in industrial control systems. In this environment, there is a pressing need for the development of cyberattack detection systems for substations and in particular protective relays, a critical component of substation operation. This article presents a deep learning-based cyberattack detection system for transmission line protective relays. The proposed cyberattack detection system is first trained with current and voltage measurements representing various types of faults on the transmission lines. The cyberattack detection system is then employed to detect current and voltage measurements that are maliciously injected by an attacker to trigger the transmission line protective relays. The proposed cyberattack detection system is evaluated under a variety of cyberattack scenarios. The results demonstrate that a universal architecture can be designed for the deep learning-based cyberattack detection systems in substations.