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Volume 2 Issue 4
Oct.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2015  >  2(4): 431-439
Song Deng, Dong Yue, Xiong Fu and Aihua Zhou, "Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 431-439, 2015.
Citation: Song Deng, Dong Yue, Xiong Fu and Aihua Zhou, "Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 431-439, 2015.
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Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming

  • 1. Research Institute of Advanced Technology, Nanjing University of Post and Telecommunications, Nanjing 210003, China;

  • 2. Computer Science and Technology, Nanjing University of Post and Telecommunications, Nanjing 210003, China;

  • 3. State Grid Smart Grid Research Institute, Beijing 102209, China

Funds:

This work was support by National Natural Science Foundation of China (61202354, 51507084) and Nanjing University of Post and Telecommunications Science Foundation (NUPTSF) (NT214203).

    Abstract
  • Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality.

     

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