An Intelligent CSO-DBNN Based Cyber Intrusion Detection Model for Smart Grid Power System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Mrs. Sabita Nayak, Mr. Amit Kumar
  10.14445/22315381/IJETT-V68I6P208S

MLA 

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.

Abstract
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) [21] 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

Reference

[1] C. h. Rowland, "Intrusion detection system." U.S. Patent 6,405,318, June 11, 2002.
[2] O. Depren, M. Topallar, E.Anarim, M. K. Ciliz, "An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks", Expert systems with Applications, Vol.29, No.4, pp.713-722, 2005.
[3] D. Bolzoni, “Revisiting Anomaly-based Network Intrusion Detection Systems”, University of Twente, Enschede, Netherlands, 2009.
[4] C. A. Catania, and C. G. Garino, "Automatic network intrusion detection: Current techniques and open issues." Computers & Electrical Engineering, Vol.38, No.5, pp.1062-1072, 2012.
[5] D. P. Vinchurkar, and A. Reshamwala, "A Review of Intrusion Detection System Using Neural Network and Machine Learning", International Journal of Engineering Science and Innovative Technology, Vol.1, No.2, pp.54-63, 2012.
[6] M. Crosbie, R. Shepley, B. Kuperman, and L.L. Frayman, "Computer architecture for an intrusion detection system", U.S. Patent 7,007,301, February 28, 2006.
[7] X. Fang, S. Misra, G. Xue, and D. Yang, "Smart grid—The new and improved power grid: A survey", IEEE communications surveys & tutorials, Vol.14, No.4, pp.944- 980, 2011.
[8] P. I. Radoglou-Grammatikis, and P. G. Sarigiannidis, "Securing the smart grid: A comprehensive compilation of intrusion detection and prevention systems", IEEE Access Vol.7, No. 1, pp.46595-46620, 2019.
[9] Q. Schueller, K. Basu, M. Younas, M. Patel, and F. Ball, "A hierarchical intrusion detection system using support vector machine for SDN network in cloud data center", In: Proc. of the International Telecommunication Networks and Applications Conference, Sydney, NSW, Australia, pp. 1-6, 2018
[10] D. H. Kang, B. K. Kim, J. T. Oh, T. Y. Nam, and J. S. Jang, "FPGA based intrusion detection system against unknown and known attacks", In: Pacific Rim International Workshop on Multi-Agents, Springer, Berlin, Heidelberg, pp. 801-806, 2006
[11] M. De Nadai, and M. van Someren, “Short-term anomaly detection in gas consumption through arima and artificial neural network forecast,” In: IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, Trento, Italy, p. 250255, 2015
[12] R. S. Naoum, and Z, N. Al-Sultani, "Learning vector quantization (LVQ) and k-nearest neighbor for intrusion classification", World of Computer Science and Information Technology Journal (WCSIT), Vol.2, No.3, pp.105-109, 2012.
[13] G. Zhao, C. Zhang, and L. Zheng, "Intrusion detection using deep belief network and probabilistic neural network", In: Proc. of the International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing, Guangzhou, China, pp. 639-642, 2017
[14] Q. Yang, D. An, R. Min, W. Yu, X. Yang, and W. Zhao, "On optimal PMU placement-based defense against data integrity attacks in smart grid", IEEE Transactions on Information Forensics and Security, Vol.12, No.7, pp. 1735-1750, 2017.
[15] M. Z. Gunduz, and R. C. Das, "Cyber-security on smart grid: Threats and potential solutions." Computer Networks, Vol.169, No.8, pp. 107094, 2020.
[16] G. Efstathopoulos, P. R. Grammatikis, P. Sarigiannidis, V. Argyriou, A. Sarigiannidis, K. Stamatakis, M. K. Angelopoulos, and S.K. Athanasopoulos, In: Proc. of the International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, Limassol, Cyprus, Cyprus, pp. 1-6, 2019
[17] S. Binna, S. R. Kuppannagari, D. Engel, and V. K. Prasanna, "Subset Level Detection of False Data Injection Attacks in Smart Grids", In: Proc. of the IEEE Conference on Technologies for Sustainability, Long Beach, CA, USA, USA, pp. 1-7, 2018
[18] I. Aljarah, H. Faris, and S. Mirjalili, "Optimizing connection weights in neural networks using the whale optimization algorithm", Soft Computing, Vol.22, No.1, pp.1-15, 2018.
[19] E. M. Hassib, A. I. El-Desouky, L. M. Labib, and E. S. Elkenawy, "WOA+ BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network", Soft Computing, Vol.24, No.1, pp. 5573–5592, 2020.
[20] B. Genge, P. Haller, C. D. Dumitru, and C. Enăchescu, "Designing optimal and resilient intrusion detection architectures for smart grids", IEEE Transactions on Smart Grid, Vol.8, No.5, pp.2440-2451, 2017.
[21] D, Rodrigues, X. S. Yang, and J. P. Papa, “Fine-tuning deep belief networks using cuckoo search, Bio-Inspired Computation and Applications in Image Processing”, Chapter 3, pp. 47-59, Academic Press, 2016.
[22] S. C. Chu, P. W. Tsai PW, and J.S. Pan, "Cat swarm optimization", In: Proc. of Pacific Rim International Conference on Artificial Intelligence, Springer, Berlin, Heidelberg, pp. 854-858, 2006
[23] M. Z. Alom , V. Bontupalli, and T. M. Taha "Intrusion detection using deep belief networks", In: Proc. of International Conference on National Aerospace and Electronics, Dayton, OH, USA, pp. 339-344, 2015

Keywords
Deep Belief Neural Networks, Cat Swarm Optimization, Intrusion Detection System, Smart Grid (SG) power system