MACHINE LEARNING METHODS FOR ATTACK DETECTION IN THE  SMART GRID

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe

MACHINE LEARNING METHODS FOR ATTACK DETECTION IN THE SMART GRID

Authors

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe

Keywords:

Smart Grid, Cybersecurity, Machine Learning, Attack Detection, False Data Injection, Intrusion Detection System, Deep Learning, Artificial Intelligence.

Abstract

The rapid evolution of smart grids hassignificantly improved the efficiency,reliability, and automation of modern powersystems by integrating advancedcommunication networks, Internet of Things(IoT) devices, and intelligent monitoringtechnologies. However, this increasedconnectivity has also expanded the attacksurface, making smart grids vulnerable to cyberattacks such as false data injection

References

[1] M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, "Machine Learning Methods for Attack Detection in the Smart Grid," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1773–1786, 2016.

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Published

2024-09-20

How to Cite

Dr. Gurudev Sawarkar ,DR. Shilpa R. Kalambe. (2024). MACHINE LEARNING METHODS FOR ATTACK DETECTION IN THE SMART GRID. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9393–9401. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5728