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Enhancing Security Gaps in Smart Grid Communication
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 Title & Authors
Enhancing Security Gaps in Smart Grid Communication
Lee, Sang-Hyun; Jeong, Heon; Moon, Kyung-Il;
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 Abstract
In order to develop smart grid communications infrastructure, a high level of interconnectivity and reliability among its nodes is required. Sensors, advanced metering devices, electrical appliances, and monitoring devices, just to mention a few, will be highly interconnected allowing for the seamless flow of data. Reliability and security in this flow of data between nodes is crucial due to the low latency and cyber-attacks resilience requirements of the Smart Grid. In particular, Artificial Intelligence techniques such as Fuzzy Logic, Bayesian Inference, Neural Networks, and other methods can be employed to enhance the security gaps in conventional IDSs. A distributed FPGA-based network with adaptive and cooperative capabilities can be used to study several security and communication aspects of the smart grid infrastructure both from the attackers and defensive point of view. In this paper, the vital issue of security in the smart grid is discussed, along with a possible approach to achieve this by employing FPGA based Radial Basis Function (RBF) network intrusion.
 Keywords
Cost ratio;Device implant attacks;RBF;Smart grid;
 Language
English
 Cited by
 References
1.
A. Saif, A. B. Zubair, Fuzzy-based optimization for effective detection of smart grid cyber-attacks, Interna tional Journal of Smart Grid and Clean Energy, vol.1, no.1, Sept., 2012.

2.
D. Simon, Training radial basis neural networks with the extended Kalman filter, Neurocomputing, 48, 455-475, 2002. crossref(new window)

3.
Z. Koldovsky and P. Tichavsky. Blind instantaneous noisy mixture separation with best interference-plus-noise rejection. InPro ceedings of the 7th international conference on Independent component analysis and signal separation, pp. 730-737(2007).

4.
L. Husheng, M. Rukun, L. Lifeng, and R. Qiu. Compressed Meter Reading for Delay-Sensitive and Secure Load Report in Smart Grid. In Smart Grid Communications, 2010. SmartGridComm 2010. First IEEE International Conference on, (2010).

5.
D. Donoho, Compressed sensing, Information Theory, IEEE Transactions on. 52(4), 1289-1306, (2006).

6.
R. C. Qiu. Cognitive Radio and Smart Grid. Invited presentation at IEEE Chapter (2010). URL http://iweb.tntech.edu/rqiu.