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Data Analytics and Decision Support for Cybersecurity pp 223–241Cite as

Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems Using Gaussian Processes and Fuzzy Inference

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Abstract

The future of electric power is associated with the use of information technologies. The smart grid of the future will utilize communications and big data to regulate power flow, shape demand with a plethora of pieces of information and ensure reliability at all times. However, the extensive use of information technologies in the power system may also form a Trojan horse for cyberattacks. Smart power systems where information is utilized to predict load demand at the nodal level are of interest in this work. Control of power grid nodes may consist of an important tool in cyberattackers’ hands to bring chaos in the electric power system. An intelligent system is proposed for analyzing loads at the nodal level in order to detect whether a cyberattack has occurred in the node. The proposed system integrates computational intelligence with kernel modeled Gaussian processes and fuzzy logic. The overall goal of the intelligent system is to provide a degree of possibility as to whether the load demand is legitimate or it has been manipulated in a way that is a threat to the safety of the node and that of the grid in general. The proposed system is tested with real-world data.

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Authors and Affiliations

  1. Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, West Lafayette, IN, USA

    Miltiadis Alamaniotis & Lefteri H. Tsoukalas

Authors
  1. Miltiadis Alamaniotis
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  2. Lefteri H. Tsoukalas
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Correspondence to Miltiadis Alamaniotis .

Editor information

Editors and Affiliations

  1. University of Bristol, Bristol, United Kingdom

    Iván Palomares Carrascosa

  2. Centre for Secure Information Technologies, Queen’s University of Belfast, Belfast, United Kingdom

    Harsha Kumara Kalutarage

  3. Queen’s University Belfast, Belfast, United Kingdom

    Yan Huang

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Alamaniotis, M., Tsoukalas, L.H. (2017). Learning from Loads: An Intelligent System for Decision Support in Identifying Nodal Load Disturbances of Cyber-Attacks in Smart Power Systems Using Gaussian Processes and Fuzzy Inference. In: Palomares Carrascosa, I., Kalutarage, H., Huang, Y. (eds) Data Analytics and Decision Support for Cybersecurity. Data Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-59439-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-59439-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59438-5

  • Online ISBN: 978-3-319-59439-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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