Cluster Computing

, Volume 22, Supplement 3, pp 7677–7685 | Cite as

M optimal routes hops strategy: detecting sinkhole attacks in wireless sensor networks

  • Zhaohui ZhangEmail author
  • Sanyang Liu
  • Yiguang Bai
  • Yalin Zheng


Sinkhole malicious nodes attacks are quite hard to handle in wireless sensor networks, due to the reason that the results of the attacks may cut off the communications by spreading false hops, absorbing data packets. Those terrible attacks are formed from the unique destroying method, which will preferentially destroy the nodes with higher communication function and shorter distance from the Sink node (Base station). In this paper, we propose a secure and energy-efficient detection scheme which can detect the malicious nodes more precisely than the traditional methods. In particularly, a new measure method is introduced in this paper, which is the frequency of each node by establishing M routes with optimal hops from per node to the Sink node. Using the proposed measure and dynamic programming, the malicious nodes can easily tell apart from the network according to the various satisfaction levels and hop difference. Compared with the traditional algorithms, simulation results show that our scheme can significantly promote the detection rate and the false positive rate. The detection rate increased about by 6–30%, the false positive rate decreased about by 5–25%. We also obtain a high energy-saving results in wireless sensor networks.


Wireless sensor networks Sinkhole attack Node detection Dynamic programming 



This work was supported by the National Natural Science Foundation of China (61373174) and the Fundamental Research Funds for the Central Universities (JB150716).


  1. 1.
    Li, H., Li, K., Qu, W., Stojmennovic, I.: Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks. Future Gener. Comput. Syst. 37, 108–116 (2014)CrossRefGoogle Scholar
  2. 2.
    Lu, W., Gong, Y., Liu, X., et al.: Collaborative energy and information transfer in green wireless sensor networks for smart cities. IEEE Trans. Ind. Inf. 99, 1–1 (2017)Google Scholar
  3. 3.
    Mishra, A.K., Turuk, A.K.: A comparative analysis of node replica detection schemes in wireless sensor networks. J. Netw. Comput. Appl. 61, 21–32 (2016)CrossRefGoogle Scholar
  4. 4.
    Xiao, B., Yu, B., Gao, C.: CHEMAS: identify suspect nodes in selective forwarding attacks. J. Parallel Distrib. Comput. 67, 1218–1230 (2007)CrossRefGoogle Scholar
  5. 5.
    Kavitha, R.J., Caroline, B.E.: Secured and reliable data transmission on multi-hop wireless sensor network. Clust. Comput. 2, 1–10 (2017)Google Scholar
  6. 6.
    Salehi, S.A., Razzaque, M.A., Naraei, P., Farrokhtala, A.: Detection of sinkhole attack in wireless sensor networks. In: Proceedings of the 2013 IEEE International Conference on Space Science and Communication, pp. 361–365 (2013)Google Scholar
  7. 7.
    Ibrahim, A., Rahman, M.M., Roy, M.C.: Detecting sinkhole attacks in wireless sensor network using hop count. Int. J. Comput. Netw. Inf. Secur. 7(3), 50–56 (2015)Google Scholar
  8. 8.
    Tumrongwittayapak, C., Varakulsiripunth, R.: Detecting sinkhole attacks in wireless sensor networks. In: ICROS-SICE International Joint Conference, pp. 1966–1971 (2009)Google Scholar
  9. 9.
    Qi, J., Hong, T., Kuang, X.H., Liu, Q.: Detection and defense of sinkhole attack in wireless sensor network. In: 2012 IEEE 14th International Conference on Communication Technology (ICCT), pp. 809–813 (2012)Google Scholar
  10. 10.
    Han, G.J., Jiang, J.F., Shen, W., Shu, L., Rodrigues, J.: IDSEP: a novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks. Inst. Eng. Technol. 72, 97–105 (2013)Google Scholar
  11. 11.
    Zhang, F.J., Zhai, L.D., Yang, J.C., Cui, X.: Sinkhole attack detection based on redundancy mechanism in wireless sensor networks. Procedia Comput. Sci. 31, 171–720 (2014)Google Scholar
  12. 12.
    Ye, N., Chen, Q.: An anomaly detection technique based on a Chi square statistic for detecting intrusions into information systems. Qual. Reliab. Eng. Int. 17(2), 105–112 (2001)CrossRefGoogle Scholar
  13. 13.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless micro sensor networks. In: Proceedings of the 33rd Hawaii International Conference on System Sciences, pp. 1–10 (2000)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Zhaohui Zhang
    • 1
    Email author
  • Sanyang Liu
    • 1
  • Yiguang Bai
    • 1
  • Yalin Zheng
    • 1
  1. 1.School of Mathematics and StatisticsXidian UniversityXi’anChina

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