Authors :
Dr. A. Manjula; M.Sai Prasad; B. Pragnya; D. Sahithya; K. Pranay; K. Avinash; M. Pradeep
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3HzJWRj
DOI :
https://doi.org/10.5281/zenodo.7888939
Abstract :
Creating a cyber security strategy for active
distribution systems is challenging due to the integration
of distributed renewable energy source. This essay
presents a methodology for adaptive hierarchical cyberattack localisation and detection for distributed active
distribution systems utilising electrical waveform
analysis. The foundation for cyber attack detection is a
sequential deep learning model, which enables the
detection of even the tiniest cyberattacks. The two-stage
approach first estimates the cyber-attack sub-region
before localising the specified cyber-attack within it. For
the "coarse" localization of hierarchical cyber-attacks,
we propose a modified spectral clustering-based method
of network partitioning. Second, it is recommended to
use a normalised impact score based on waveform
statistical metrics to further pinpoint the location of a
cyber attack by defining various waveform
features.Finally, a detailed quantitative evaluation using
two case studies shows that the proposed framework
produces good estimation results when compared to
established and cutting-edge approaches.
Keywords :
SVM, Random Forest, Gradient Boosting, Logistic Regression, Cyber Attack Detection.
Creating a cyber security strategy for active
distribution systems is challenging due to the integration
of distributed renewable energy source. This essay
presents a methodology for adaptive hierarchical cyberattack localisation and detection for distributed active
distribution systems utilising electrical waveform
analysis. The foundation for cyber attack detection is a
sequential deep learning model, which enables the
detection of even the tiniest cyberattacks. The two-stage
approach first estimates the cyber-attack sub-region
before localising the specified cyber-attack within it. For
the "coarse" localization of hierarchical cyber-attacks,
we propose a modified spectral clustering-based method
of network partitioning. Second, it is recommended to
use a normalised impact score based on waveform
statistical metrics to further pinpoint the location of a
cyber attack by defining various waveform
features.Finally, a detailed quantitative evaluation using
two case studies shows that the proposed framework
produces good estimation results when compared to
established and cutting-edge approaches.
Keywords :
SVM, Random Forest, Gradient Boosting, Logistic Regression, Cyber Attack Detection.
