False data injection attacks with incomplete information



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False data injection attacks have recently been introduced as an important class of cyber attacks against smart grid's wide area measurement and monitoring systems. These attacks aim to compromise the readings of multiple power grid sensors and phasor measurement units in order to mislead the operation of the control centers. Recent studies have shown that if an adversary has complete knowledge on the power grid topology and transmission-line admittance values, he can adjust the false data injection attack vector such that the attack remains undetected and it successfully passes the residue-based bad data detection tests that are commonly used in power system state estimation. However, in this thesis, we explain that a realistic false data injection attack is essentially an attack with incomplete information due to the attackers lack of real-time knowledge with respect to various grid parameters and attributes such as the position of circuit breaker switches and transformer tap changers and also because of the attacker's limited physical access to most grid facilities. In this thesis, we have shown the minimum required information for a perfect false data injection attack. Then, we mathematically characterize false data injection attacks with incomplete information from both the attacker's and grid operator's viewpoints. Furthermore, we introduce a novel vulnerability measure that can compare and rank different power grid topologies against such attacks. To the best of our knowledge, this work is the first study to investigate false data injection attacks with line admittance uncertainty.