Skip to main content
padlock icon - secure page this page is secure

Intrusion Detection in Smart Grid Using Machine Learning Approach

Buy Article:

$106.46 + tax (Refund Policy)

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.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

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

More about this publication?
  • 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.
  • Editorial Board
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Terms & Conditions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more