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Intrusion Detection in Smart Grid Using Machine Learning Approach

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One among the most unpredictable cyber physical systems accessible around the globe is Smart Grid. The fundamental motivation behind smart grid is to utilize transmission alongside distribution framework to keep up satisfactory stream of power from generation systems to end clients. The disturbances in power are exceptionally erratic on account of the utilization of different characteristic and man-made sources. Control administrator are for the most part dependent on the unsettling influences of power system for their response and after that make specific response based on these occasions. Humans can’t identify the different cyber-attacks over the power frameworks, since attackers are sufficiently brilliant to cover the tracks. To recognize the different cyber-attacks and separating different power system aggravations, Machine learning is an improving method utilized and consequently people, using this strategy, can make better decisions regarding the cause for power disturbances by focusing mainly on the cyber-attacks. Therefore, we investigate different Machine learning strategies as a methods for Detection and classification of these attacks and furthermore examine practical utilization of these procedures as an improvement to past power system architectures.
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Keywords: Machine Learning; SCADA; Smart Grid; Synchrophasors

Document Type: Research Article

Affiliations: Department of Computer Science, Sharda University, Greater Noida 201310, UP, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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