Open Access Open Access  Restricted Access Subscription or Fee Access

SVM and GMM Based Unsupervised User-Behavior Evaluation Method for Heterogeneous Trustworthy Network

L. Maria Michael Visuwasam, J. Indra Mercy


Trustworthy network is an inevitable trend in the development of high trusted computing and Internet. Behavior evaluation is an important research topic in trustworthy network. Till now, most effect focuses on the validity of host’s and user’s identity, such as integrity measurement and access control, which could not guarantee the trustworthiness of valid user’s behavior. In this paper, we proposed an unsupervised method for evaluating user’s network behavior and trustworthiness grades in a local heterogeneous network. First, we collected network behavior samples as more as possible. Then, they were tagged with different trustworthiness grades. According to the graded sample data, our method constructed a GMM model to evaluate user’s latter network behavior. And this model is again compared the performance under Support vector machine. The system can be deployed in a corporation indicated that our method could evaluate trustworthiness of users based on their network behaviors.


Behavior Evaluation, Clustering, Network Behavior, Support Vector Machine, Trusted Computing.

Full Text:



Trusted Computing Group. [Online]. Available:, 2009.

C. Lin and X.H. Pen, “Research on trustworthy networks,” Chinese Journal of Computers, China, vol. 28, pp. 751–758, May, 2005 (in Chinese).

L.Q. Tian and C. Lin, “A kind of game-theoretic control mechanism of user behavior trust based on prediction in trustworthy network,” Chinese Journal of Computers, China, vol. 30, pp. 1930–1938, November 2007 (in Chinese).

X.Y. Li, X.L. Gui, Q. Mao, and D.Q. Leng, “Adaptive dynamic trust measurement and prediction model based on behavior monitoring,” Chinese Journal of Computers, China, vol. 32, pp. 664–674, April, 2009 (in Chinese).

M.C. Fern´andez-Gago, R. Rom´an, and J. Lopez. “A survey on the applicability of trust management systems for wireless sensor networks,” in Proc of 3rd International Workshop on Security, Privacy and Trust in Pervasive and Ubiquitous Computing, 2007, pp. 25–30.

Y.H. Ding, Q.Z. Li, and F.F. Li, “A novel method for evaluating trustworthiness between strangers in large, dynamic Ad Hoc networks,” in Proc of the 2th International Workshop on Knowledge Discovery and Data Mining, 2009, pp. 280–283.

J. Wang, Y.H. Liu, J. Zhang, and Y. Jiao, “Evaluating access terminal in trusted network. testing and diagnosis,” in Proc of IEEE Circuits and Systems International Conference on ICTD, 2009, pp.1–4.

J. Wang, Y.H. Liu, J. Zhang, J.Q. Zhu, and T.Z. Dong, “Research on hierarchy trusted network based on the grade division,” in Proc of the

rd International Conference on Intelligent System and Knowledge Engineering, 2008, pp. 19–25.

S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed., Academic Press, 2009.

J.A.K Suykens, T. Van Gestel, and J. De Brabanter, Least squares support vector machines, Singapore Island, World Scientific Publishing Co Pte Lte, 2002.

C.P. Liu, M.Y. Fan, G.W. Wang, and S.L. Ma. “Optimizing parameters of support vector machine based on gradient algorithm,” Control and Decision, China, vol. 23, pp. 1291–1296, November 2008 (in Chinese).

Wang Guo-yin and Yao Yi-yu, Yu Hong, the theory of rough set and the summary of research of application[J]Science News of Compute,2009,7.

Xu Xi and Yao Qiong-hui, Shi Min, The intelligent faults classification approach based on rough set and support vector machine [J] Computing Technology and Automation 2006 25(3):32- 34.

Zhang Guo-yun Zhang Jing A hybrid RS-SVM dynamic prediction approach to rotary kiln sintering process [C] Proceedings of 2004 International Conference on Machine Learning and Cybernetics Hangzhou China IEEE 2004 1478-1482.

Liang Hong-xia, Yan De-le, Support vector machine of rough set [J] computer science 2009,4:36(4).

Lee Ki-Young KimDae-Won.Possibilistic support vector machines. Pattern Recognition 2005 38(8) 1325-1327.

Philip Kotler and Kevin Lane Keller, “A Framework for Marketing Management,”Peking University Press, 2007.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.