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Research Article
Detection of Eavesdropper in Sybil Attack using DV-HOP Algorithm
Dr. A.S. Shanthi1
G. Mona Jacqueline2
P. Nethaji3
M. Dhanish4
S. Gowtham55
B. Gnanamani6
123456Department of Computer Science and Engineering, Tamilnadu College of Engineering, Tamilnadu, India.
Published Online: July-August 2022
Pages: 149-151
Cite this article
No DOIReferences
[1]. M.F. Arani, A.A. Jahromi, D. Kundur and M. Kassouf, “Modelling and simulation of the aurora attack on micro grid point of common
coupling,”in 2019 7th workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES). IEEE, 2019, pp. 1-6.
[2]. R. Tan, V. Badrinath Krishna, D.K. Yau and Z. Kalbarczyk, “Impact of integrity attacks on real-time pricing in smart grids,” 2013, pp.
439-450.
[3]. Y. Dafalla, B. Liu, A.A Hahn, H.Wu, R. Ahmadi and A.G. Bardas, “Prosumer nanogrids: A cybersecurity assessment,” IEEE Access,
vol. 8, pp. 131 150 – 131 164, 2020, event IEEE Access.
[4]. B. Liu and H. Wu, “Optimal d-facts placement in moving target defense against false data injection attacks,” IEEE Transactions on
Smart Grid, pp. 1-1, 2020, event: IEEE Transactions on smart grid.
[5]. Z. Zhang, R. Deng, D.K Yau, P. Cheng and J. Chen, “Analysis of moving target defense against false data injection attacks on power
grid,” IEEE Transactions on Information Forensics and Security, vol. 15, pp.2320 – 2335, 2019.
[6]. S. Lakshminarayana and D.K. Yau, “Cost-benefit analysis of moving target defense in power grids,” IEEE Transactions on Power
Systems, 2020.
[7]. J. Tian, R. Tan, X. Guan and T. Liu, “Enhanced hidden moving target defense in smart grids,” IEEE Transactions on Smart Grid, vol.
10, no. 2, pp. 2208 – 2223, 3 2019 44.
[8]. X. Niu, J. Li , J. Sun and K. Tomsovic “Dynamic detection of false data injection attack in smart grid using deep learning,” in 2019
IEEE Power Energy Society Innovative Smart Grid Technologies Conference ( ISGT), 2019, pp. 1-6.
[9]. Y. Li, Y. Wang and S. Hu, “Online generative adversary network based measurement recovery in false data injection attacks: A cyber-
physical approach,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2031-2043, 2020.
[10]. G. Ding, Q. Wu, Y.D. Yao, J. Wang and Y.Chen, “Kernel-based learning for statistical signal processing in cognitive radio
networks: Theoretical foundations, example applications, and future directions,” IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 126-
136, 2019.
coupling,”in 2019 7th workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES). IEEE, 2019, pp. 1-6.
[2]. R. Tan, V. Badrinath Krishna, D.K. Yau and Z. Kalbarczyk, “Impact of integrity attacks on real-time pricing in smart grids,” 2013, pp.
439-450.
[3]. Y. Dafalla, B. Liu, A.A Hahn, H.Wu, R. Ahmadi and A.G. Bardas, “Prosumer nanogrids: A cybersecurity assessment,” IEEE Access,
vol. 8, pp. 131 150 – 131 164, 2020, event IEEE Access.
[4]. B. Liu and H. Wu, “Optimal d-facts placement in moving target defense against false data injection attacks,” IEEE Transactions on
Smart Grid, pp. 1-1, 2020, event: IEEE Transactions on smart grid.
[5]. Z. Zhang, R. Deng, D.K Yau, P. Cheng and J. Chen, “Analysis of moving target defense against false data injection attacks on power
grid,” IEEE Transactions on Information Forensics and Security, vol. 15, pp.2320 – 2335, 2019.
[6]. S. Lakshminarayana and D.K. Yau, “Cost-benefit analysis of moving target defense in power grids,” IEEE Transactions on Power
Systems, 2020.
[7]. J. Tian, R. Tan, X. Guan and T. Liu, “Enhanced hidden moving target defense in smart grids,” IEEE Transactions on Smart Grid, vol.
10, no. 2, pp. 2208 – 2223, 3 2019 44.
[8]. X. Niu, J. Li , J. Sun and K. Tomsovic “Dynamic detection of false data injection attack in smart grid using deep learning,” in 2019
IEEE Power Energy Society Innovative Smart Grid Technologies Conference ( ISGT), 2019, pp. 1-6.
[9]. Y. Li, Y. Wang and S. Hu, “Online generative adversary network based measurement recovery in false data injection attacks: A cyber-
physical approach,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2031-2043, 2020.
[10]. G. Ding, Q. Wu, Y.D. Yao, J. Wang and Y.Chen, “Kernel-based learning for statistical signal processing in cognitive radio
networks: Theoretical foundations, example applications, and future directions,” IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 126-
136, 2019.
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