Andic, C.Ozturk, A.Turkay, B.2024-08-232024-08-232023979-835036049-3https://doi.org/10.1109/ELECO60389.2023.10416015https://hdl.handle.net/20.500.12684/1472814th International Conference on Electrical and Electronics Engineering, ELECO 2023 -- 30 November 2023 through 2 December 2023 -- Virtual, Bursa -- 197135In energy systems, measurement accuracy is jeopardized by bad data arising from cyber attacks. When bad data is detected in the measurement dataset as a result of cyber attacks, it's essential to identify and eliminate these data. However, this elimination process introduces the problem of missing measurement data, threatening the system's observability conditions. This study proposes a data mining approach supported by artificial neural networks to address the missing measurement data issue when bad data is detected. Our proposed method aims to maintain the system's observability by completing the measurement data lost due to bad data. Consequently, the measurement set purified from bad data enhances the accuracy of the crow search algorithm based state estimation results. This methodology has been shown to successfully mitigate the adverse effects of unforeseen situations, such as cyber attacks. © 2023 IEEE.en10.1109/ELECO60389.2023.10416015info:eu-repo/semantics/closedAccessANNcrow search algorithmcyber attacksstate estimationComputer crimeCrimeCyber attacksData miningLearning algorithmsNetwork securityNeural networksObservabilityANNBad dataCrow search algorithmCyber-attacksEnergy systemsMeasurement dataMissing measurementsPowerSearch AlgorithmsSystem observabilityState estimationConference Object2-s2.0-85185833835N/A