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Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics,
Source : 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Institute of Electrical and Electronics Engineers Inc., Volume 2017-January, p.198-204 (2017)
ISBN : 9781509063673
Keywords : Artificial intelligence, Big data, Big Data Analytics, Classification (of information), Decision trees, Deep learning, Deep neural networks, Dimensionality reduction algorithms, Electric power transmission networks, Electromagnetic waves, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Machine learning techniques, Mercury (metal), Monitor and control, naive Bayes classification, Phasor measurement units, Smart grid, Smart power grids, Technological advancement, Units of measurement
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2017
Abstract : Technological advancement enables the need of internet everywhere. The power industry is not an exception in the technological advancement which makes everything smarter. Smart grid is the advanced version of the traditional grid, which makes the system more efficient and self-healing. Synchrophasor is a device used in smart grids to measure the values of electric waves, voltages and current. The phasor measurement unit produces immense volume of current and voltage data that is used to monitor and control the performance of the grid. These data are huge in size and vulnerable to attacks. Intrusion Detection is a common technique for finding the intrusions in the system. In this paper, a big data framework is designed using various machine learning techniques, and intrusions are detected based on the classifications applied on the synchrophasor dataset. In this approach various machine learning techniques like deep neural networks, support vector machines, random forest, decision trees and naive bayes classifications are done for the synchrophasor dataset and the results are compared using metrics of accuracy, recall, false rate, specificity, and prediction time. Feature selection and dimensionality reduction algorithms are used to reduce the prediction time taken by the proposed approach. This paper uses apache spark as a platform which is suitable for the implementation of Intrusion Detection system in smart grids using big data analytics.
Cite this Research Publication : K. Vimalkumar and Dr. Radhika N., “A big data framework for intrusion detection in smart grids using apache spark”, in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, 2017, vol. 2017-January, pp. 198-204.