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An Optimal Group Decision-Making Approach for Cyber Security Using Improved Selection-Drift Dynamics

Author

Listed:
  • Enning Zhang

    (Air Force Engineering University)

  • Gang Wang

    (Air Force Engineering University)

  • Runnian Ma

    (Air Force Engineering University)

  • Juan Li

    (Air Force Engineering University)

Abstract

In cyber security, balancing investment and risk has always been a dilemmatic problem since the threats often lurk in the shadows. Thus, timely and scientifically credible decision-making will have great significance in such an attack-defense game of incomplete information. To this end, an evolutionary game model of group decision-making is proposed to analyze the behavioral change process of the defender population. In particular, we first introduce the concept of observation error and short-term prediction in network communication and establish an improved selection–drift dynamics in which errors are automatically corrected to a certain extent and the convergence speed is faster than the prototype. By calculating the stable evolutionary equilibrium of the defender population, the optimal group decision-making approach is formulated. Case studies on big data vulnerabilities indicate that the proposed model and approach perform better in robustness and computational efficiency than the 3 typical models under jamming environments.

Suggested Citation

  • Enning Zhang & Gang Wang & Runnian Ma & Juan Li, 2023. "An Optimal Group Decision-Making Approach for Cyber Security Using Improved Selection-Drift Dynamics," Dynamic Games and Applications, Springer, vol. 13(3), pages 980-1004, September.
    • Handle: RePEc:spr:dyngam:v:13:y:2023:i:3:d:10.1007_s13235-022-00476-6
    DOI: 10.1007/s13235-022-00476-6
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    References listed on IDEAS

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    1. Pengxi Yang & Fei Gao & Hua Zhang, 2021. "Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics," Mathematics, MDPI, vol. 9(23), pages 1-18, November.
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