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Cyber Defense Strategy

Decent Essays
Abstract — This paper presents a resilient defense strategy to protect the power system state estimation against false data injection (FDI) attacks. The proposed strategy is based on calculation of the risk of the attack and the optimal budget allocation on the measurements. The method has been formulated as a mixed integer nonlinear programming (MINLP) problem. Multiple researchers have addressed the same problem but with the assumption that some meter measurements can be fully protected or without considering the risk of the attack. The validation of the proposed method has also been evaluated based on various IEEE standard test systems, including IEEE 5-bus, 9-bus, 14-bus and 39-bus system.
Index Terms—Cyber-attacks, resiliency, state
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Multiple research presented defense strategies against FDIA attacks not only to protect the system but also increase its cyber resiliency [12], [18], [14]. Resiliency as defined in literature is the system’s ability to plan/prepare, absorb, recover from and adapt to known and unknown threats [18]. For example, a self-healing PMU network to achieve resiliency against cyber-attacks is introduced in a software-defined networking (SDN) infrastructure in [12]. The configuration of the network switches will change to isolate the compromised measurements to prevent further propagation of the attack. This strategy has its own drawbacks such as not being practical because of too much latency of the data to reach at the operational and decision centers. The delay due to several reconfigurations will make the power system more vulnerable to unwanted blackouts due to false and late estimated state measurements. The introduced method also increases maintenance cost and failure rates of the switches. Another strategy proposed is the optimal protection method presented in [5]. This method protects the state variables with the minimum number of measurements, as a variant Steiner tree problem in a graph. This result in an N-P hard problem that needs to be solved each time bad data is detected, requiring huge processing and computation resources. The authors in [6] proposed
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