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Mr. Sani Umar, Full-Time COE MS Student, will defend his MS Thesis on Thursday, November 26, 2020, at 10:00 a.m. Online (By Using Microsoft Teams). His MS thesis title is "Detection of False Data Injection Attacks Against Optimal Power Flow in Power Systems". His thesis advisor is "Dr. Muhamad Felemban, Assistant Professor, COE Department". You are cordially invited to attend by clicking the following link ""
Abstract:                                                                                                                                                                                                                                                                                           Nowadays, the cyber-security of modern power systems has captured a significant interest. This is because the world has seen a surge in cyber-attacks on power systems. Such attacks lead to the Russian Trinitrotoluene (TNT) explosion in 1982, the USA generator explosion in 2007, the Turkey oil explosion in 2008, the energy generation and distribution problem in Saudi Arabia and Qatar in 2012, and the Ukraine blackout in 2015. This problem arises due to the integration of cyber components into power systems. Cyber components enhance power systems operation and provide better decision making and monitoring. The vulnerabilities in the cyberinfrastructure of the power systems provide an avenue for adversaries to launch cyber-attacks. An example of such cyber-attacks is False Data Injection Attacks (FDIA). Such an attack may lead to catastrophic consequences that disturb the normal operation of the power systems. In some cases, the attacks can lead to blackouts. The main contribution of this research is to analyze the impact of FDIA on the cost of power generation and the physical component of the power systems and develop a detection and prevention system to mitigate such attacks. To achieve this, we have introduced a new FDIA strategy that intends to maximize the cost of power generation and proposed a rule-based detection and prevention system with primary and secondary attack detection features. The Viability of the attacks and the detection and prevention systems is shown using simulations on the standard IEEE bus systems using MATPOWER MatLab package. We have used the Genetic Algorithm (GA), Simulated Annealing (SA) algorithm, Tabu Search (TS), and Particle Swarm Optimization (PSO) to find the suitable attack targets and execute the attacks in the power systems. Moreover, the attacks are capable of increasing the power system generation cost which corresponds to the Optimal Power Flow (OPF) objective function on different standard IEEE bus systems. From the attacker's perspective, the proposed FDIA increases the power generation cost by up to 15.6%, 45.1%, 60.12%, and 74.02% on the standard IEEE 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. Accuracy metrics are used to evaluate the performance of the developed rule-based FDIA detection and prevention system. We have found that the False Negatives (FN) rates become significantly low as the value of load demand threshold τpd increases. Specifically, the FN rates approaches 0 when τpd > 130MW, τpd > 300MW, τpd > 150MW, and τpd > 1200MW for the standard IEEE 6-bus, 9-bus, 30-bus, and 118-bus systems, respectively. 

Expiry: 01 Jan 2021