Keywords
cyber-physical security
clustering algorithm
dynamic persistent trust
How to Cite
Abstract
Due to the rapid development of network technology and the increasing size of networks, the security of networks in today’s society is facing great challenges. The study of network security is based on the study of network security frameworks, connections, and models to improve network security so that the network becomes controllable, manageable, and survivable. In this paper, we propose a dynamic continuous trust assessment model based on the clustering mechanism and apply this model to the network security model optimization problem. The specific conclusions are as follows: (1) The problems in previous clustering algorithms are analyzed, and corresponding solutions are proposed for the problems. Then, the particle swarm algorithm is briefly introduced, the inertia weight coefficients in the particle swarm algorithm are improved, and the dormant mechanism of neighboring node groups is introduced. (2) A new algorithm for calculating direct trust value is proposed. (3) A method for evaluating the overall trust value level of the network is proposed. The method first selects the N nodes with the highest number of interactions and then evaluates the overall trust level of the network in which these N nodes are located by using the associative memory capability of the clustering algorithm. (4) The experimental analysis shows that the randomly generated rule set in the model of this paper tends to evolve to a stable state after 60∼80 rounds of operation, with a higher success rate, and at 180∼200 rounds of operation, the rule set evolves to a stable state again, with the success rate still improved; after 240 rounds of operation, the network environment is restored to the initial state, and although the roles of some of the nodes are changed, the model still evolves to a high level again. Level. The dynamic continuous trust evaluation model proposed in this paper can be effective in the network security optimization, but the practical application of the model should be done further research.
References
Liang Hua, Li Yang, Lei Juan, et al., 2022, Research on the evaluation method of Internet of Things nodes based on fuzzy evidence theory[J]. Journal of Southwest Normal University(Natural Science Edition), 47(03):111–124. DOI: 10.13718/j.cnki.xsxb.2022.03.013.
Li Huanhuan, Xu Xiaoyun, Wang Honglei, 2021, Research on the architecture and application of network security model based on zero trust[J]. Science and Technology Information, 19(17):7–9. DOI: 10.16661/j.cnki.1672-3791.2107-5042-9133.
Li Jinhua, Wang Hu, Lei Jianjun, 2019, Trust assessment model based on entropy weight method in trusted networks[J]. Journal of Central China Normal University (Natural Science Edition), 53(01):26–29. DOI: 10.19603/j.cnki.1000-1190.2019.01.005.
Liu He, Wang Wei-Ying, Zhang Benli, 2014, Heterogeneous wireless network security model based on trust and risk assessment[J]. Journal of Heilongjiang Engineering Institute, 2014, 28(06):40–44. DOI: 10.19352/j.cnki.issn1671-4679.2014.06.011.
Sinaga, K. P., and Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727.
Xu, D., and Tian, Y. (2015). A comprehensive survey of clustering algorithms. Annals of data science, 2, 165–193.
Dhanachandra, N., Manglem, K., and Chanu, Y. J. (2015). Image segmentation using the K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764–771.
Yuan, C., and Yang, H. (2019). Research on the K-value selection method of the K-means clustering algorithm. J, 2(2), 226–235.
Nayak, J., Naik, B., and Behera, H. (2015). Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In Computational Intelligence in Data Mining-Volume 2: Proceedings of the International Conference on CIDM, 133–149.
Yuan, G., Sun, P., Zhao, J., Li, D., and Wang, C. (2017). A review of moving object trajectory clustering algorithms. Artificial Intelligence Review, 47, 123–144.
El Alami, H., and Najid, A. (2017). Fuzzy logic-based clustering algorithm for wireless sensor networks. International Journal of Fuzzy System Applications (IJFSA), 6(4), 63–82.
Zhang, D., Ge, H., Zhang, T., Cui, Y. Y., Liu, X., and Mao, G. (2018). New multi-hop clustering algorithm for vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 20(4), 1517–1530.
Murtagh, F., and Contreras, P. (2017). Algorithms for hierarchical clustering: an overview, II. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6), 1219.
Wang, D., Tan, D., and Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft computing, 22(2), 387–408.
Piotrowski, A. P., Napiorkowski, J. J., and Piotrowska, A. E. (2020). Population size in particle swarm optimization. Swarm and Evolutionary Computation, 58, 100718.
Ali, A. F., and Tawhid, M. A. (2017). A hybrid particle swarm optimization and genetic algorithm with population partitioning for large-scale optimization problems. Ain Shams Engineering Journal, 8(2), 191–206.
Zhou, F. H., and Liao, Z. Z. (2013). A particle swarm optimization algorithm. Applied Mechanics and Materials, 303, 1369–1372.
Juneja, M., and Nagar, S. K. (2016, October). Particle swarm optimization algorithm and its parameters: A review. In 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM), 1–5.
Lynn, N., Ali, M. Z., and Suganthan, P. N. (2018). Population topologies for particle swarm optimization and differential evolution. Swarm and evolutionary computation, 39, 24–35.
Zhang, D., Ma, G., Deng, Z., Wang, Q., Zhang, G., and Zhou, W. (2022). A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology. Applied Soft Computing, 130, 109660.
Nayak, P., and Devulapalli, A. (2015). A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.
Bures, T., Hnetynka, P., Heinrich, R., Seifermann, S., and Walter, M. (2020). Capturing dynamicity and uncertainty in security and trust via situational patterns. In Leveraging Applications of Formal Methods, Verification and Validation: Engineering Principles: 9th International Symposium on Leveraging Applications of Formal Methods, 9, 295–310.
De Visser, E. J., Peeters, M. M., Jung, M. F., Kohn, S., Shaw, T. H., Pak, R., and Neerincx, M. A. (2020). Towards a theory of longitudinal trust calibration in human-robot teams. International journal of social robotics, 12(2), 459–478.
Stavroulaki, V., Strinati, E. C., Carrez, F., Carlinet, Y., Maman, M., Draskovic, D., … and Demestichas, P. (2021). DEDICAT 6G-Dynamic coverage extension and distributed intelligence for human-centric applications with assured security, privacy, and trust: From 5G to 6G. In 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 556–561.
Ling Xiangrong (2023), Based on attention neural network and Bayesian optimization [D]. Xidian University. DOI: 10.27389/d.cnki.gxadu.2023.001113.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Journal of Cyber Security and Mobility