An Intelligent CSO-DBNN Based Cyber Intrusion Detection Model for Smart Grid Power System
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2020 by IJETT Journal|
|Year of Publication : 2020|
|Authors : Mrs. Sabita Nayak, Mr. Amit Kumar
|DOI : 10.14445/22315381/IJETT-V68I6P208S|
MLA Style: Mrs. Sabita Nayak, Mr. Amit Kumar "An Intelligent CSO-DBNN Based Cyber Intrusion Detection Model for Smart Grid Power System" International Journal of Engineering Trends and Technology 68.6(2020):50-57.
APA Style:Mrs. Sabita Nayak, Mr. Amit Kumar. An Intelligent CSO-DBNN Based Cyber Intrusion Detection Model for Smart Grid Power System International Journal of Engineering Trends and Technology, 68(6),50-57.
Massive proliferation in cyber-attacks has drawn much attention today among researches and network users of different arena sectors. After all these years of research aftereffects against cyberattack, still we lack in reliable Intrusion Detection System (IDS) which can adjust by itself for bulk amount of data based on the real time situations. In this manuscript, we present an IDS model to classify the different class of cyber-attack scenarios such as short circuit, relay setting change, remote command input, false data injection, line maintenance, and failure detection in SG power-system. Furthermore, a preprocessing, data normalization, feature selection are carried out and finally, after applying Gaussian random distribution the taxonomy here is done by Cat Swarm Optimization (CSO)  algorithm trained Deep Belief Neural Network (DBNN) with minimum MSE. At last accuracy, precision, recall, and F1 score metrics are analyzed to show the reliability of our proposed CSO assimilated Machine learning based intrusion detection systems in a SG power system
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Deep Belief Neural Networks, Cat Swarm Optimization, Intrusion Detection System, Smart Grid (SG) power system