A dynamic trust evaluation model based on optimized hidden markov process

被引:0
作者
Gao, Yan [1 ,2 ]
Liu, Wenfen [1 ,2 ]
机构
[1] School of Cyberspace Security, PLA Info. Eng. Univ., Zhengzhou
[2] State Key Lab. of Mathematical Eng. and Advanced Computing, Zhengzhou
来源
Sichuan Daxue Xuebao (Gongcheng Kexue Ban)/Journal of Sichuan University (Engineering Science Edition) | 2015年 / 47卷 / 03期
关键词
Adjustment space; Dynamic trust; Harmony search algorithm; Hidden Markov process; Trust degree;
D O I
10.15961/j.jsuese.2015.03.014
中图分类号
学科分类号
摘要
In order to depict high dynamic of entity behavior quickly and accurately, a trust evaluation model based on continuous-time hidden Markov process was proposed. Different from the trust models on discrete-time hidden Markov chain, this model fully considered time dependence of trust, combined time intervals between the interactions and made the trust evaluation problem boil down to the learning problem of continuous-time hidden Markov process. Then an algorithm for solving the optimal parameters of hidden Markov process was given with the improved harmony algorithm, which could effectively guarantee the global search space and achieve a better solution. On this basis, the trust degree could be predicted using the existing interaction sequences and optimal parameters. Simulation results showed that the model is able to quickly reflect the dynamic of entity behavior, has high accuracy and resists the malicious attacks. ©, 2015, Editorial Department of Journal of Sichuan University. All right reserved.
引用
收藏
页码:101 / 107
页数:6
相关论文
共 11 条
[1]  
Wang J., Sun B., Niu X., Et al., Distributed trust model based on parameter modeling, Journal on Communications, 34, 4, pp. 47-59, (2013)
[2]  
Ayday E., Fekri F., Iterative trust and reputation management using belief propagation, IEEE Transactions on Dependable and Secure Computing, 9, 3, pp. 375-386, (2012)
[3]  
Elsalamouny E., Krukow K.T., Sassone V., An analysis of the exponential decay principle in probabilistic trust models, Theoretical Computer Science, 410, pp. 4067-4084, (2009)
[4]  
Moe M.E.G., Helvik B.E., Knapskog S.J., Comparison of the beta and the hidden Markov models of trust in dynamic environments, Trust Management III, IFTP Advances in Information and Communication Technology 300, pp. 283-297, (2009)
[5]  
Liu X., Datta A., Modeling context aware dynamic trust using hidden Markov model, Proceedings of the 26th AAAI Conference on Artificial Intelligence, pp. 1938-1944, (2012)
[6]  
Liu C., Yacine O., Antoine N., Et al., The reputation evaluation based on optimized hidden Markov model in E-commerce, Mathematical Problems in Engineering, 2013, pp. 1-11, (2013)
[7]  
Elsalamouny E., Sassone V., An HMM-based reputation model, Advances in Security of Information and Communication Networks, 381, pp. 111-121, (2013)
[8]  
Turin W., Continuous time HMM, Performance Analysis and Modeling of Digital Transmission Systems, (2004)
[9]  
Geem Z.W., Kim J.H., Loganathan G.V., A new heuristic optimization algorithm: Harmony Search, Simulation, 76, 2, pp. 60-68, (2001)
[10]  
Geem Z.W., Sim K.B., Parameter-setting-free harmony search algorithm, Applied Mathematics and Computation, 217, pp. 3881-3889, (2010)