Vulnerability Assessment and Spectral Analysis based Detection for Smart Grid Cyber Physical System Security

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Name of the Speaker: Ms. Amulya (EE17D003)
Guide: Dr. K.S Swarup
Venue/Online meeting link:
Date/Time: 13th January 2023 (Friday), 2:00 PM

The power system becomes an attractive choice for cyber attacks because of many interconnections. Thus, it is crucial to understand attack vulnerabilities and their impact on the system. This research presents the Vulnerability Assessment (VA) of grid control systems to data injection cyber-attacks (CA). The analysis, carried out from a power system engineer's perspective, illustrates a successful stealth attack that can be implemented on the system with minimal system knowledge by injecting false data into sensor and actuator measurements. A combination of an eavesdropping attack (EDA) and a False Data Injection Attack (FDIA) is used in the attack model. Further, a singular spectrum analysis-based method extracts system's dynamics under regular operation. A projection-based distance tracking method is proposed for detecting grid control system attacks. Two variations of the algorithm, viz-a-viz, single-variate and multi-variate algorithms, are proposed for detection at different levels of the power system. The proposed methodology is robust, adaptive, and computationally efficient, especially considering system and measurement noises. The three main features of the proposed method are: (i) uses standard SCADA data and does not require attack data, (ii) can be integrated with the existing grid control system with minimal hardware, and (iii) is independent of system configuration and upgrades. Formal hypothesis testing is also proposed to determine the detection threshold and also make the method adaptive. The attack detection algorithm can successfully detect different attacks, including stealth attacks on multiple sensors. The algorithm is tested on an IEEE 39-bus New England test system, 300 bus test system, and 1888 bus RTE system. IoT-based hardware was used for establishing the robustness of the algorithm. The proposed method was found to be reliable, fast, robust, and scalable under noisy measurements compared to existing methods.