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Network Security Situation Assessment Model Based on Extended Hidden Markov

Author

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  • Yiwei Liao
  • Guosheng Zhao
  • Jian Wang
  • Shu Li

Abstract

A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.

Suggested Citation

  • Yiwei Liao & Guosheng Zhao & Jian Wang & Shu Li, 2020. "Network Security Situation Assessment Model Based on Extended Hidden Markov," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, August.
  • Handle: RePEc:hin:jnlmpe:1428056
    DOI: 10.1155/2020/1428056
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    Cited by:

    1. Baoshan Xie & Fei Li & Hao Li & Liya Wang & Aimin Yang, 2023. "Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM," Mathematics, MDPI, vol. 11(16), pages 1-15, August.
    2. Xiaoling Tao & Kaichuan Kong & Feng Zhao & Siyan Cheng & Sufang Wang, 2020. "An efficient method for network security situation assessment," International Journal of Distributed Sensor Networks, , vol. 16(11), pages 15501477209, November.

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