Computer Security Active Defense Technology Based on Bayesian Model-Reference-Cited by-同舟云学术

Computer Security Active Defense Technology Based on Bayesian Model

Published:2023-06-19 Issue:2 Volume:8 Page:2433-2442
ISSN:2444-8656
Container-title:Applied Mathematics and Nonlinear Sciences
language:en
Short-container-title:

Author:

Liu Zhiyu¹,Ma Baoying²,Xing Jun¹,Cao Wangcheng¹

Affiliation:

1. 1 Department of Computer Science and Technology , Mudanjiang Normal University , Mudanjiang, Heilongjiang, 157011 , China .

2. 2 College of Health Management , Mudanjiang Medical University , Mudanjiang, Heilongjiang, 157011 , China .

Abstract

Abstract The existing active defense technology based on game theory is mainly based on static games. This method can not adapt to the dynamic change of the attacker’s attacking intent and tactics. Based on the non-cooperative and non-zero-sum dynamic game theory, a complete Bayesian system of emotional game bodies is established. Use the “virtual node” to transform the attack and defense chart into a tree structure of the attack and defense game. An attack and defense strategy suitable for integrity and incompleteness is given. The experimental simulation results show that the seven comprehensibilities and usability of Nash equilibrium solutions optimized by Bayesian subgames are well obtained

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Link

https://www.sciendo.com/pdf/10.2478/amns.2023.1.00424

Reference11 articles.

1. Alexander, R. (2020). Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures. Journal of Information Security, 11(3), 121-137.

2. Hu, H., Liu, J., Tan, J., & Liu, J. (2020). SOCMTD: selecting optimal countermeasure for moving target defense using dynamic game. KSII Transactions on Internet and Information Systems (TIIS), 14(10), 4157-4175.

3. Khakzad, N. (2021). A Tutorial on Fire Domino Effect Modeling Using Bayesian Networks. Modelling, 2(2), 240-258.

4. Zheng, Y., Hu, X., & Sun, H. (2021). Research on Motion Control for a Mobile Robot Using Learning Control Method. Applied Mathematics and Nonlinear Sciences, 6(1), 227-234.

5. Rajesh Kanna, M., Pradeep Kumar, R., Nandappa, S., & Cangul, I. (2020). On Solutions of Fractional order Telegraph Partial Differential Equation by Crank-Nicholson Finite Difference Method. Applied Mathematics and Nonlinear Sciences, 5(2), 85-98.