Skip to main content

Blockchain-Based Sinkhole Attack Detection in Wireless Sensor Network

  • Chapter
  • First Online:
Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations

Abstract

In past years with upgrades in sensor technology, new networking streams and wireless communication with fast networks are applied to the Wireless Sensor Networks (WSN). It raised the region of WSN to a wide reach that led to its massive usage in areas like biotechnology, military applications, IoT, etc. Because of the self-administering nature of sensors in WSN environments, the sensor hubs are also open to compromise. Exactly when a hub is compromised, a few attacks are possible, for instance, spoofing and sinkhole attacks. In this work, the sinkhole attack in WSN is designed and Artificial Bee Colony – Attack Detection (ABC-AD) mechanism for perceiving the sinkhole attack in WSN is proposed. The combination of blockchain and the Artificial Bee Colony is used for evaluating the precedented area of sinkhole attack using the rule-based identical methodology and voting-based methodology. The proposed method shows good resistance and detects sinkholes with a 2% increase in performance when compared with other techniques. The proposed methodology is intuited by the shortfalls of the existing algorithms and the effectiveness of Gestalt consciousness algorithms in the recent era leading to an optimized Artificial Bee Colony exchange security mechanism with blockchain.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192–219.

    Article  Google Scholar 

  2. Shafiei, H., Khonsari, A., Derakhshi, H., & Mousavi, P. (2014). Detection and mitigation of sinkhole attacks in wireless sensor networks. Journal of Computer and System Sciences, 80(3), 644–653.

    Article  MATH  Google Scholar 

  3. Dallas, D., Leckie, C., & Rao, K. R. (2007). Hop-count monitoring: Detecting sinkhole attacks in wireless sensor networks. In 15th IEEE international conference on Networks, 2007. ICON 2007 (pp. 176–181). IEEE.

    Chapter  Google Scholar 

  4. Salehi, S. A., Razzaque, M. A., Naraei, P., & Farrokhtala, A. (2013). Detection of sinkhole attack in wireless sensor networks. In 2013 IEEE international conference on Space Science and Communication (IconSpace) (pp. 361–365). IEEE.

    Chapter  Google Scholar 

  5. Keerthana, G., & Padmavathi, G. (2016). Detecting sinkhole attack in wireless sensor network using enhanced particle swarm optimization technique. International Journal of Security and Its Applications, 10(3), 41–54.

    Article  Google Scholar 

  6. Zhang, F.-J., Zhai, L.-D., Yang, J.-C., & Cui, X. (2014). Sinkhole attack detection based on redundancy mechanism in wireless sensor networks. Procedia Computer Science, 31, 711–720.

    Article  Google Scholar 

  7. Singh, T., & Arora, H. K. (2013). Detection and correction of sinkhole attack with the novel method in WSN using NS2 tool. International Journal of Advanced Computer Science and Applications, 4(2).

    Google Scholar 

  8. Bahekmat, M., Yaghmaee, M. H., Yazdi, A. S. H., & Sadeghi, S. (2012). A novel algorithm for detecting sinkhole attacks in WSNs. International Journal of Computer Theory and Engineering, 4(3), 418.

    Article  Google Scholar 

  9. Ngai, E. C. H., Liu, J., & Lyu, M. R. (2007). An efficient intruder detection algorithm against sinkhole attacks in wireless sensor networks. Elsevier Computer Communications, 30, 2353–2364.

    Article  Google Scholar 

  10. Xie, M., Han, S., Tian, B., & Parvin, S. (2011). Anomaly detection in wireless sensor networks: A survey. Journal of Network and Computer Applications, 34(4), 1302–1325.

    Article  Google Scholar 

  11. Wazid, M., Das, A. K., Kumari, S., & Khan, M. K. (2016). Design of sinkhole node detection mechanism for hierarchical wireless sensor networks. Security and Communication Networks, 9(17), 4596–4614.

    Article  Google Scholar 

  12. Soni, V., Modi, P., & Chaudhri, V. (2013). Detecting sinkhole attack in a wireless sensor network. International Journal of Application or Innovation in Engineering & Management, 2(2), 29–32.

    Google Scholar 

  13. Sharmila, S., & Umamaheswari, G. (2011). Detection of sinkhole attack in wireless sensor networks using message digest algorithms. In 2011 International conference on Process Automation, Control and Computing (PACC) (pp. 1–6). IEEE.

    Google Scholar 

  14. Jahandoust, G., & Ghassemi, F. (2017). An adaptive sinkhole aware algorithm in wireless sensor networks. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2017.01.002

  15. Guo, Q., Li, X., Xu, G., & Feng, Z. (2016). MP-MID: Multi-Protocol Oriented Middleware-level intrusion detection method for wireless sensor networks. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.06.010

  16. Xie, J., Yang, T., Yang, F., Pan, L., Xu, J., & Yao, H. (2018). Intrusion detection system for hybrid dos attacks using energy Trust in wireless sensor networks. Procedia Computer Science, 131, 1188–1195.

    Article  Google Scholar 

  17. Borkara, G. M., Patil, L. H., Dalgadec, D., & Hutke, A. (2019). A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept. Sustainable Computing: Informatics and Systems, 23, 120–135.

    Google Scholar 

  18. Hajiheidari, S., Wakil, K., Badri, M., & Navimipour, N. J. (2019). Intrusion detection systems in the Internet of Things: A comprehensive investigation. Computer Networks, 160, 165–191.

    Article  Google Scholar 

  19. Pundir, S., Wazid, M., Singh, D. P., Das, A. K., Rodrigues, J. J. P. C., & Park, Y. (2020). Designing efficient sinkhole attack detection mechanism in edge-based IoT deployment. Sensors, 20, 1300. https://doi.org/10.3390/s20051300

    Article  Google Scholar 

  20. Sejaphala, L. C., & Velempini, M. The design of a defense mechanism to mitigate sinkhole attack in software-defined wireless sensor cognitive radio networks. Wireless Personal Communications, 113, 977–993. https://doi.org/10.1007/s11277-020-07263-9

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gaya, D., Parthiban, L., Nithiyanandam, N. (2024). Blockchain-Based Sinkhole Attack Detection in Wireless Sensor Network. In: Goundar, S., Anandan, R. (eds) Integrating Blockchain and Artificial Intelligence for Industry 4.0 Innovations. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-35751-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35751-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35750-3

  • Online ISBN: 978-3-031-35751-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics