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Analysis of Wireless Sensor Networks Behavior for Trustworthiness Evaluation

  • Ying ZhangEmail author
  • Peisong Li
  • Jun Wang
  • Mingxing Wang
  • Dengpan Ye
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 960)

Abstract

Wireless sensor networks (WSNs) face many security challenges in their applications. In order to improve the security of WSNs, a trust security algorithm based on nodes behavior analysis and cloud model is proposed. According to the behavior characteristics of the conventional attacks, three kinds of trust factors are defined and introduced to the trust security algorithm: the transmission rate factor, the spatial correlation factor and the replay attack factor. The cloud model is used to judge the security status of the nodes according to these three trust factors. In the comprehensive calculation of the trust value, the time attenuation factor and the strategy for excluding the impersonation factor by historical evaluations are introduced. Moreover, the influence of the impersonation factor is further excluded by considering the acceptance domain of the trust distribution, and the defamatory nodes could get punished finally. Simulation experiments show that the proposed algorithm can detect the malicious nodes, identify the impersonation nodes, and resist on impersonation attacks effectively.

Keywords

Wireless sensor networks Trust security Malicious nodes Cloud model Trust factors 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (no. 61673259, U1636101, U1636219, U1736211); International Exchanges and Cooperation Projects of Shanghai Science and Technology Committee (No. 15220721800); partially supported by the National Key Research Development Program of China (2016QY01W0200), also supported in part by the US National Science Foundation Grant CCF-1337244.

References

  1. 1.
    Rao, W., Hu, Y., Hu, Z., Xiong, S.: Towed vector fiber optic sensor used in ocean seismic exploration. In: Proceedings of SPIE - The International Society for Optical Engineering, pp. 4–16 (2012)Google Scholar
  2. 2.
    Luque-Nieto, M.A., Moreno-Roldán, J.M., Poncela, J., Otero, P.: Optimal fair scheduling in S-TDMA sensor networks for monitoring river plumes. J. Sens. 2016(5), 1–6 (2016)CrossRefGoogle Scholar
  3. 3.
    Mythrehee, H., Julian, A.: A cross layer UWSN architecture for marine environment monitoring. In: 2015 Global Conference on Communication Technologies, Communication Technologies (GCCT), Thuckalay, India, pp. 211–216 (2015)Google Scholar
  4. 4.
    Jin, Y., Kwak, K.S., Sengoku, M., Shinoda, S.: Wide area sensor network for disaster prevention and monitoring: concept and service coverage. In: 2014 IEEE Asia Pacific Conference on Circuits and Systems, Circuits and Systems (APCCAS), Ishigaki, Japan, pp. 391–394 (2014)Google Scholar
  5. 5.
    Lee, S.H., Lee, S., Song, H., Lee, H.S.: Wireless sensor network design for tactical military applications: remote large-scale environments. In: 2009 Military Communications Conference, Boston, MA, USA, pp. 911–917 (2009)Google Scholar
  6. 6.
    Sharma, V., Hussain, M.: Mitigating replay attack in wireless sensor network through assortment of packets. In: Satapathy, S.C., Prasad, V.K., Rani, B.P., Udgata, S.K., Raju, K.Srujan (eds.) Proceedings of the First International Conference on Computational Intelligence and Informatics. AISC, vol. 507, pp. 221–230. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-2471-9_22CrossRefGoogle Scholar
  7. 7.
    Azam, S., Manzoor, R., Rehman, M.: Secure solution to data transfer from sensor node to sink against aggregator compromises. In: 2011 Frontiers of Information Technology (FIT), Islamabad, Pakistan, pp. 247–252 (2011)Google Scholar
  8. 8.
    Bysani, L.K., Turuk, A.K.: A survey on selective forwarding attack in wireless sensor networks. In: 2011 International Conference on Devices and Communications (ICDeCom), Devices and Communications, Mesra, India, pp. 24–25 (2011)Google Scholar
  9. 9.
    Mahajan, M., Reddy, K.T.V., Rajput, M.: Design and simulation of a blacklisting technique for detection of hello flood attack on LEACH protocol. Procedia Comput. Sci. 79, 675–682 (2016)CrossRefGoogle Scholar
  10. 10.
    Pongaliur, K., Xiao, L., Liu, A.X.: Dynamic camouflage event based malicious node detection architecture. J. Supercomput. 64(3), 717–743 (2013)CrossRefGoogle Scholar
  11. 11.
    Tian, B., Yao, Y., Shi, L., Shao, S., Liu, Z., Xu, C.: A novel sybil attack detection scheme for wireless sensor network. In: 5th IEEE International Conference on Broadband Network & Multimedia Technology, Broadband Network and Multimedia Technology (IC-BNMT), Guilin, China, pp. 294–297 (2013)Google Scholar
  12. 12.
    Yang, G., Ying, S., Yang, W.: Reputation model based on behaviors of sensor nodes in WSN. J. Commun. 30(12), 18–26 (2009)Google Scholar
  13. 13.
    Upadhyay, R., Bhatt, U.R., Tripathi, H.: DDOS attack aware DSR routing protocol in WSN. Procedia Comput. Sci. 78, 68–74 (2016)CrossRefGoogle Scholar
  14. 14.
    Fakhrey, H., Tiwari, R., Johnston, M., Al-Mathehaji, Y.A.: The optimum design of location-dependent key management protocol for a WSN with a random selected cell reporter. IEEE Sens. J. 16(19), 7217–7226 (2016)CrossRefGoogle Scholar
  15. 15.
    Tufail, A., Khan, A.M., Kim, K.H.: A reliable and secure hybrid key management scheme for WSNs. J. Int. Technol. 16(4), 629–642 (2015)Google Scholar
  16. 16.
    Baburaj, E.: Polynomial and multivariate mapping-based triple-key approach for secure key distribution in wireless sensor networks, 59, 274–290 (2017)Google Scholar
  17. 17.
    Pirzada, A.A., McDonald, C., Datta, A.: Performance comparison of trust-based reactive routing protocols, 5(6), 695–710 (2006)Google Scholar
  18. 18.
    Salehi, M., Boukerche, A., Darehshoorzadeh, A.: Towards a novel trust-based opportunistic routing protocol for wireless networks, 22(3), 1–17 (2016)Google Scholar
  19. 19.
    Junwei, W., Xiaoyi, F.: Improved TEEN based trust routing algorithm in WSNs. In: 27th Chinese Control and Decision Conference (CCDC), Qingdao, China, pp. 4379–4382 (2015)Google Scholar
  20. 20.
    Raza, S., Haider, W., Durrani, N.M., Khan, N.K., Abbasi, M.A.: Trust based energy preserving routing protocol in multi-hop WSN. In: Bouajjani, A., Fauconnier, H. (eds.) NETYS 2015. LNCS, vol. 9466, pp. 518–523. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26850-7_42CrossRefGoogle Scholar
  21. 21.
    Chen, Z., He, M., Liang, W., Chen, K.: Trust-aware and low energy consumption security topology protocol of wireless sensor network, 2015(1), 1–10 (2015)Google Scholar
  22. 22.
    Li, D., Liu, C., Gan, W.: A new cognitive model: cloud model, 24(3), 357–375 (2009)Google Scholar
  23. 23.
    Lu, H., Pi, E., Peng, Q., Wang, L., Zhang, C.: A particle swarm optimization-aided fuzzy cloud classifier applied for plant numerical taxonomy based on attribute similarity, 36(5), 9388–9397 (2009)Google Scholar
  24. 24.
    Sajjad, S.M., Bouk, S.H., Yousaf, M.: Neighbor node trust based intrusion detection system for WSN, 63, 183–188 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ying Zhang
    • 1
    Email author
  • Peisong Li
    • 1
  • Jun Wang
    • 2
  • Mingxing Wang
    • 1
  • Dengpan Ye
    • 3
  1. 1.Shanghai Maritime UniversityShanghaiChina
  2. 2.University of Central FloridaOrlandoUSA
  3. 3.Wuhan UniversityWuhanChina

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