Skip to main content
SpringerLink
Log in

A Malicious Node Identification Strategy with Environmental Parameters Optimization in Wireless Sensor Network

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the wireless sensor network (WSN), nodes show a low forwarding rate under a poor-quality links environment and a resource-constrained state. The malicious nodes imitate this forwarding behavior, which can selectively forward date, eavesdropping, or discarding important dates. The traditional reputation model is challenging to identify with this kind of sub-attack nodes. To address these problems, a malicious node identification strategy based on time reputation model and environmental parameters optimization (TRM-EPO) is proposed in the WSN. First of all, the comprehensive reputation is calculated according to the direct reputation and the recommended indirect reputation. The environmental parameters matrix is based on nodes’ running state, taking into account nodes’ energy, data volume, number of adjacent nodes, and node sparsity. Besides, according to the environmental parameters matrix, and the recorded comprehensive reputation matrix, the next cycle’s trust can be predicted. Finally, a similarity of the actual reputation and predicted trust matrix is proposed to compare with an adaptive threshold to identify malicious nodes. The experimental results demonstrate that the proposed strategy improves sensor nodes’ security and reliability in a complex environment. Moreover, compared to comparison algorithms, the TRM-EPO improves the recognition rate above 1% and reduces the false-positive rate by more than 1%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Sahoo, R. R., Ray, S., Sarkar, S., et al. (2018). Guard against trust management vulnerabilities in wireless sensor network. Arabian Journal for Science and Engineering, 43(12), 7229–7251.

    Article  Google Scholar 

  2. Jin, X., Liang, J., Tong, W., et al. (2017). Multi-agent trust-based intrusion detection scheme for wireless sensor networks. Computers and Electrical Engineering, 59, 262–273.

    Article  Google Scholar 

  3. Bao, F., Chen, I. R., Chang, M. J., et al. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE Transactions on Network and Service Management, 9(2), 169–183.

    Article  Google Scholar 

  4. Feng, R., Han, X., Liu, Q., et al. (2015). A credible bayesian-based trust management scheme for wireless sensor networks. International Journal of Distributed Sensor Networks, 11(11), 1–9.

    Article  Google Scholar 

  5. Osanaiye, O., Alfa, A. S., & Hancke, G. P. (2018). Denial of service (DoS) defence for resource availability in wireless sensor networks. IEEE Access, 6, 6975–7004.

    Article  Google Scholar 

  6. Siddiqui, S., Ghani, S., & Khan, A. A. (2018). PD-MAC: Design and implementation of polling distribution-MAC for improving energy efficiency of wireless sensor networks. International Journal of Wireless Information Networks, 25(1), 1–9.

    Article  Google Scholar 

  7. Elshrkawey, M., Elsherif, S. M., & Elsayed Wahed, M. (2018). An enhancement approach for reducing the energy consumption in wireless sensor networks. Journal of King Saud University-Computer and Information Sciences, 30(2), 259–267.

    Article  Google Scholar 

  8. Zawaideh, F., & Salamah, M. (2019). An efficient weighted trust-based malicious node detection scheme for wireless sensor networks. International Journal of Communication Systems, 32(3), 1–13.

    Article  Google Scholar 

  9. Ganeriwal, S., & Srivastava, M. (2004). Reputation-based framework for high integrity sensor networks. In Proceeding of the 2nd ACM workshop on security of ad hoc and sensor networks (SASN’04), New York, USA, ACM (pp. 66–77).

  10. Ishmanov, F., Kim, S. W., et al. (2015). A robust trust establishment scheme for wireless sensor networks. Sensors (Basel, Switzerland), 15(3), 7040–7061.

    Article  Google Scholar 

  11. Xi, O., Bin, T., Dong, L., et al. (2012). A novel hierarchical reputation model for wireless sensor networks. International Journal of Digital Content Technology and its Applications, 6(10), 61–69.

    Article  Google Scholar 

  12. Zhang, L., Yin, N., & Wang, R. (2015). Research of malicious nodes identification based on DPAM-DM algorithm for WSN. Journal on Communications, 36(S1), 53–59.

    Google Scholar 

  13. Fu, J. S., & Liu, Y. (2015). Double cluster heads model for secure and accurate data fusion in wireless sensor networks. Sensors, 15(1), 2021–2040.

    Article  MathSciNet  Google Scholar 

  14. Zhang, Z., Zhu, H., Luo, S., et al. (2017). Intrusion detection based on state context and hierarchical trust in wireless sensor networks. IEEE Access, 5(99), 12088–12102.

    Article  Google Scholar 

  15. Ting, H., Shoushan, L., Yang, X., et al. (2013). Dynamic trust model with multiple decision factors in MANET. Journal of Beijing University of Posts and Telecommunications, 36(5), 104–109.

    Google Scholar 

  16. Ram Prabha, V., & Latha, P. (2017). Fuzzy trust protocol for malicious node detection in wireless sensor networks. Wireless Personal Communications, 94(4), 2549–2559.

    Article  Google Scholar 

  17. Teng, Z., Guo, L., Lv, J., et al. (2019). WSN bayes reputation evaluation model based on time series information analysis. Journal of Zhengzhou University (Engineering Science), 40(1), 38–43.

    Google Scholar 

  18. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 3rd annual Hawaii international conference on system sciences (HICSS’00), Hawaii, USA (pp. 1–10).

  19. Li, Y., Yu, J., & You, X. (2013). An incentive protocol for opportunistic networks with resources constraint. Chinese Journal of Computers, 36(05), 947–956.

    Article  Google Scholar 

  20. Yu, Y., Li, K., Zhou, W., et al. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867–880.

    Article  Google Scholar 

  21. Li, P., Shi, Z., & Liu, X. (2018). Application of fuzzy C-means clustering algorithm based on immune genetic algorithm. Journal of Northeast Electric Power University, 38(3), 79–83.

    Google Scholar 

  22. Kumar, G. E. P., Titus, I., & Thekkekara, S. I. (2012). A comprehensive overview on application of trust and reputation in wireless sensor network. Procedia Engineering, 38, 2903–2912.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper. Besides, this work is supported by the National Natural Science Foundation Youth Science Foundation Project (No. 61501107), “13th Five-Year” Scientific Research Planning Project of Jilin Province Department of Education (No. JJKH20180439KJ).

Author information

Authors and Affiliations

  1. Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, 132012, Jilin Province, China

    Zhijun Teng

  2. School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, Jilin Province, China

    Zhijun Teng, Baohe Pang & Luying Xie

  3. School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, Jilin Province, China

    Mingyang Sun

  4. Ericsson (Xi’an) Information and Communication Technology Service Co., Ltd., Dalian Branch, Dalian, 116000, Liaoning Province, China

    Liwen Guo

Authors
  1. Zhijun Teng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baohe Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingyang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luying Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liwen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baohe Pang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Teng, Z., Pang, B., Sun, M. et al. A Malicious Node Identification Strategy with Environmental Parameters Optimization in Wireless Sensor Network. Wireless Pers Commun 117, 1143–1162 (2021). https://doi.org/10.1007/s11277-020-07915-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07915-w

Keywords

Search

Navigation