THE SIGNIFICANCE OF LOCALIZATION AND ADAPTIVE HIERARCHICAL CYBER ATTACK DETECTION IN AN ACTIVE DISTRIBUTION SYSTEM (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2020)
The increasing integration of distributed renewable energy sources into the grid has made it more challenging to design a cyber security plan for active distribution networks. This article outlines a method for locating and identifying cyberattacks in distributed active distribution systems using electrical waveform analysis. A sequential deep learning model is the foundation for cyber attack detection since it can identify even the smallest incursions. In a two-stage approach, the targeted cyberattack is first localized inside the estimated cyberattack sub-region. We introduce a network splitting strategy for "coarse" localization of hierarchical cyberattacks, based on a modified version of spectral clustering. It is recommended to create several waveform parameters and apply a normalised effect score based on statistical metrics of the waves themselves in order to further localise the origin of a cyberattack.Lastly, a comprehensive quantitative evaluation based on two case studies shows that, in comparison to both traditional and cutting-edge approaches, the suggested framework produces reliable estimations. Suggested Search Terms: Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting, and Cyber Attack Detection.