Optimal network defense strategy selection based on Bayesian game

被引：0
|
作者：
Wang Z.-G. [1 ]
Lu Y. [1 ]
Li X. [1 ]
机构：
[1] Shijiazhuang Campus of Army Engineering University, Shijiazhuang
来源：
International Journal of Security and Networks | 2020年 / 15卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Attack-defence payoffs; Bayesian game; Defence effectiveness; Incomplete information; Nash equilibrium; Network attack-defence process; Network security; Optimal defence strategy; Pure strategy; Strategy selection;
D O I：
10.1504/IJSN.2020.106830
中图分类号：
学科分类号：
摘要：
Existing passive defence methods cannot effectively guarantee network security; to solve this problem, a novel method is proposed that selects the optimal defence strategy. The network attack-defence process is modelled based on the Bayesian game. The payoff is quantified from the impact value of the attack-defence actions. The optimal defence strategy is selected that takes defence effectiveness as the criterion. The rationality and feasibility of the method are verified through a representative example, and the general rules of network defence are summarised. Compared to the classic strategy selection methods based on game theory, the proposed method can select the optimal strategy in the form of pure strategy by quantifying defence effectiveness, which was proven to perform better. Copyright © 2020 Inderscience Enterprises Ltd.
引用
收藏
页码：67 / 77
页数：10
相关论文
共 50 条
  • [11] A Bayesian Game to Estimate the Optimal Initial Resource Demand for Entrant Virtual Network Operators
    Al Muktadir, Abu Hena
    Kafle, Ved P.
    Martinez-Julia, Pedro
    Harai, Hiroaki
    IEICE TRANSACTIONS ON COMMUNICATIONS, 2018, E101B (03) : 667 - 678
  • [12] The Method of Trajectory Selection Based on Bayesian Game Model
    Tian, Wen
    Fang, Qin
    Zhou, Xuefang
    Yang, Fan
    SUSTAINABILITY, 2022, 14 (18)
  • [13] Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization
    Sun, Yang
    Xiong, Wei
    Yao, Zhonghua
    Moniz, Krishna
    Zahir, Ahmed
    APPLIED SCIENCES-BASEL, 2017, 7 (11):
  • [14] Network Attack Defense Awareness based on Dynamic Game
    Li, Man
    Cao, Jinjing
    INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS, 2015, 9 (07): : 69 - 77
  • [15] A Demand-Side Pricing Strategy Based on Bayesian Game
    Yang, Jie
    Tian, Zhenhua
    Ma, Kai
    2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), 2018, : 146 - 150
  • [16] Optimal Strategy in Covert Communication based on Game Theory
    Wang, Jianquan
    Tang, WanBin
    Li, Xiaoping
    Li, Shaoqian
    2019 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2019,
  • [17] The optimal privacy strategy of cloud service based on evolutionary game
    Sun, Pan Jun
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2022, 25 (01): : 13 - 31
  • [18] Information Security Defense Situation Assessment of Network Warfare Based on Dynamic Bayesian Network
    Hui Baofeng
    Jia Guoqing
    Chen Shanji
    2018 4TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT AND INFORMATION TECHNOLOGY (ICEMIT 2018), 2018, : 1385 - 1388
  • [19] The optimal privacy strategy of cloud service based on evolutionary game
    Pan Jun Sun
    Cluster Computing, 2022, 25 : 13 - 31
  • [20] A Bayesian Game Based Approach for Associating the Nodes to the Gateway in LoRa Network
    Kumari, Preti
    Gupta, Hari Prabhat
    Dutta, Tanima
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (05) : 4583 - 4592
12345