Detection of Cyber-Attack in Broad-Scale Smart Grids using Deep and Scalable Unsupervised Machine Learning System
Simran Koul1, Simriti Koul2, Prajval Mohan3, Lakshya Sharma4, Pranav Narayan5
1Simran Koul*, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
2Simriti Koul, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
3Prajval Mohan, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
4Lakshya Sharma, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
5Pranav Narayan, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
Manuscript received on July 26, 2020. | Revised Manuscript received on August 02, 2020. | Manuscript published on August 10, 2020. | PP: 335-344 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J75430891020 | DOI: 10.35940/ijitee.J7543.0891020
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Abstract: The increase in the reliability, efficiency and security of the electrical grids was credited to the innovation of the smart grid. It is also a fact that the smart grids a very dependable on the digital communication technology that in turn gives rise to undiscovered weaknesses which have to be reconsidered for dependable and coherent power distribution. In this paper, we propose an unsupervised anomaly detection which is mainly focused the statistical correlation among the data. The main aim is to create a scalable anomaly detection system suitable for huge-scale smart grids, which are capable to denote a difference between a real fault from a disruption and an intelligent cyber-attack. We have presented a methodology that applies the concept of attribute extraction by the use of Symbolic Dynamic Filtering (SDF) to decrease compilation drift whilst uncovering usual interactions among subsystems. Results of simulation obtained on IEEE 39, 118 and 2848 bus systems confirm the execution of the method, proposed in this paper, under various working conditions. The results depict a precision of almost 99 percent, along with 98 percent of true positive rate and less than 2 percent of false positive rate.
Keywords: Anomaly, Cyber-attack, Smart grid, Statistical property, Machine learning, Unsupervised learning.
Scope of the Article: Machine Learning