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

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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.

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Data Availability

The datasets generated during and/or analyzed during the current study are available in the National Vulnerability Database repository, https://nvd.nist.gov/vuln/search; AND. The datasets generated during and/or analyzed during the current study are available in the Common Weakness Enumeration (CWE) repository, http://cwe.mitre.org/index.html; AND. The datasets generated during and/or analyzed during the current study are available in the Common Attack Pattern Enumeration and Classification (CAPEC) repository, http://capec.mitre.org/; AND. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request; AND all data generated or analyzed during this study are included in this published article (and its supplementary information files).

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Acknowledgement

National Natural Science Foundation of China, Grant Number 61902426

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Authors and Affiliations

  1. Information and Navigation Institute, Air Force Engineering University, Xian, China

    Enning Zhang, Gang Wang, Runnian Ma & Juan Li

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Correspondence to Gang Wang.

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Zhang, E., Wang, G., Ma, R. et al. An Optimal Group Decision-Making Approach for Cyber Security Using Improved Selection-Drift Dynamics. Dyn Games Appl 13, 980–1004 (2023). https://doi.org/10.1007/s13235-022-00476-6

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