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A Trusted Measurement Model for Mobile Internet

  • Yong Wang
  • Jiantao SongEmail author
  • Jia Lou
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 960)

Abstract

With the explosive development of the mobile Internet, the security threats faced by the mobile Internet have grown rapidly in recent years. Since the normal operation of the mobile Internet depends on the trust between nodes, the existing trusted measurement model cannot fully and dynamically evaluate mobile Internet computing nodes, and the trust transmission has a great deal of energy consumption. Aiming at above problems, this paper proposes a trusted measurement model combining static measurement and node behavior measurement. The model is based on the computing environment measurement of the mobile Internet computing node, and is also based on node behavior measurement, combining direct and recommended trust values to complete the measurement of nodes. It can more objectively reflect the trust degree of nodes, effectively detecting malicious nodes, and ensuring the normal operation of mobile Internet services. The simulation experiment results show that this method can effectively balance the subjectivity and objectivity of trust assessment, and can quickly avoid malicious nodes and reduce the energy consumption of the trust transmission.

Keywords

Mobile Internet Trusted measurement Behavior measurement Direct trust Recommendation trust 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China The key trusted running technologies for the sensing nodes in Internet of things: 61501007, The research of the trusted and security environment for high energy physics scientific computing system: 11675199. General Project of science and technology project of Beijing Municipal Education Commission: KM201610005023.

References

  1. 1.
    CiscoVNI Mobile: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update 2013–2018 (2014)Google Scholar
  2. 2.
    Chen, F.: Research on quality of experience oriented resource management in Mobile Internet. Dissertation for Ph.D. Degree. University of Science and Technology of China, Hefei (2013)Google Scholar
  3. 3.
    Meeker, M.: 2014 Internet Trends (2014)Google Scholar
  4. 4.
    Ericsson.: Ericsson Traffic and Market Data Reports (2013)Google Scholar
  5. 5.
    Zhu, H., Du, S., Gao, Z., et al.: A probabilistic misbehavior detectionscheme towards efficient trust establishment in delay-tolerant networks. IEEE Trans. Parallel Distrib. Syst. 99, 1–6 (2013)Google Scholar
  6. 6.
    Li, X.Y., Zhou, F., Du, J., et al.: LDTS: a lightweight and dependable trust system for clustered wireless sensor networks. IEEE Trans. Inf. Forensics Secur. (2012)Google Scholar
  7. 7.
    Chang, M.J., et al.: Trust-based intrusion detection in wireless sensor networks. In: 2011 IEEE International Conference on Communications (ICC), Kyoto, Japan, 6 Jan 2011Google Scholar
  8. 8.
    He, D., Chen, C., Chan, S., et al.: A distributed trust evaluation model and its application scenarios for medical sensor networks. IEEE Trans. Inf Technol. Biomed. 16(6), 1164–1175 (2012)CrossRefGoogle Scholar
  9. 9.
    Sun, B., Shan, X.M., Wu, K., et al.: Anomaly detection based secure in-network aggregation for wireless sensor networks. IEEE Syst. J. 7(1), 13–25 (2013)CrossRefGoogle Scholar
  10. 10.
    Xiao, D.Q., Feng, J.Z., Zhou, Q., et al.: Gauss reputation framework for sensor networks. J. Commun. 29(3), 47–53 (2008)Google Scholar
  11. 11.
    Crosby, G.V., Hester, L., Pission, N.: Location-aware, trust-based detection and isolation of compromised nodes in wireless sensor networks. Int. J. Netw. Secur. 12(2), 107–117 (2011)Google Scholar
  12. 12.
    Zhang, J., Shankaran, R., Orgun, M.A., et al.: A dynamic trust establishment and management framework for wireless sensor networks. In: 2010 IEEE/IFIP 8th International Conference on Embedded and Ubiquitous Computing (EUC), Hong Kong, China, pp. 484–491 (2010)Google Scholar
  13. 13.
    Lin, C., Tian, L.Q., Wang, Y.Z.: Research on user behavior trust in trustworthy network. J. Comput. Res. Dev. 45(12), 2033–2043 (2008)Google Scholar
  14. 14.
    Hu, X.D., Yu, P.Q., Wei, Q.F.: Detection of selective forwarding attacks in the Internet of Things. J. Chongqing Univ. Posts Telecommun. 24(2), 148–152 (2012)Google Scholar
  15. 15.
    Zhan, G., Shi, W., Deng, J.: Design and implementation of TARF: a trust-aware routing framework for WSNs. IEEE Trans. Dependable Secur. Comput. 9(2), 184–197 (2012)CrossRefGoogle Scholar
  16. 16.
    Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International Conference on World Wide Web, pp. 640–651. ACM Press (2003)Google Scholar
  17. 17.
    Golbeck, J.: Computing and Applying Trust in Web-Based Social NetWorks. University of Maryland, Maryland (2005)Google Scholar
  18. 18.
    Avesani, P., Massa, P., Tiella, R.: A trust-enhanced recommender system application: Moleskiing. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 1589–1593 (2005)Google Scholar
  19. 19.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, pp. 17–24 (2007)Google Scholar
  20. 20.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, pp. 397–406 (2009)Google Scholar
  21. 21.
    Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agents Multi-Agent Syst. 16(1), 57–74 (2008)CrossRefGoogle Scholar
  22. 22.
    Bedi, P., Kaur, H., Marwaha, S.: Trust based recommender system for the semantic Web. In: Proceedings of the IJCAI 2007 (2007)Google Scholar
  23. 23.
    Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the International Conference on Information and Knowledge Management, pp. 931–940. ACM Press (2008)Google Scholar
  24. 24.
    Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 203–210. ACM Press (2009)Google Scholar
  25. 25.
    Rghioui, A., L’aarje, A., Elouaai, F., Bouhorma, M.: The Internet of Things for healthcare monitoring: security review and proposed solution. In: Proceedings of the 2014 Third IEEE International Colloquium in Information Science and Technology (CIST), pp. 384–389 (2014)Google Scholar
  26. 26.
    Sarvabhatla, M., Giri, M., Vorugunti, C.S.: Cryptanalysis of a biometric-based user authentication mechanism for heterogeneous wireless sensor networks. In: Proceedings of the 2014 Seventh International Conference on Contemporary Computing (IC3), pp. 312–317 (2014)Google Scholar
  27. 27.
    Ntalianis, K., Tsapatsoulis, N.: Remote authentication via biometrics: a robust video-object steganographic mechanism over wireless networks. IEEE Trans. Emerg. Top. Comput. 4, 156–174 (2016)CrossRefGoogle Scholar
  28. 28.
    Ren, Y., Bonkerche, A.: Performance analysis of trust-based node evaluation mechanisms in wireless and mobile ad hoc networks. In: Proceedings of the 2009 IEEE International Conference on Communications (ICC 2009), pp. 5535–5539 (2009)Google Scholar
  29. 29.
    Cai, S.B., Han, Q.L., Gao, Z.G., Yang, D.S., Zhao, J.: Research on cloud trust model for malicious node detection in wireless sensor network. J. Electron. 40(11), 2232–2238 (2012)Google Scholar
  30. 30.
    Ganeriwal, S., Balzano, L.K., Srivastava, M.B.: Reputation-based framework for high integrity sensor networks. ACM Trans. Sens. Netw. (TOSN), 15–19 (2016)Google Scholar
  31. 31.
    Kamhoua, C., Pissinou, N., Maldd, K.: Game theoretic modeling and evolution of trust in autonomous multi-hop networks: application to network security and privacy. In: Proceedings of the 2011 IEEE International Conference on Communications (ICC 2011), pp. 1–6 (2011)Google Scholar
  32. 32.
    Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 135–142 (2010)Google Scholar
  33. 33.
    Wang, D., Ma, J., Lian, T., Guo, L.: Recommendation based on weighted social trusts and item relationships. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 254–259. ACM Press (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Information Technology DepartmentBeijing Capital International Airport Co., Ltd.BeijingChina
  2. 2.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  3. 3.China International Data System Co., Ltd.BeijingChina

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