Skip to main content
SpringerLink
Log in

Learning-Based Security Technique for Selective Forwarding Attack in Clustered WSN

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Selective forwarding attacks in WSN can damage many mission-critical applications, like military surveillance and forest fire censoring. In such attacks, malicious nodes most of the time functions like regular nodes, but sometimes drop sensitive packets selectively, like a packet recording the dissimilar power' activity, making it more difficult to identify their malicious intent. The current selective forwarding attack detection schemes, randomly select checkpoint nodes, available in-between nodes within a forwarding route, which are responsible for producing acknowledgments for each received packet. In this paper, the complete sets of nodes are differentiated into three different types based on their functionality as Inspector Node (IN), Cluster Head (CH), and Member Nodes (MN). The newly considered node as IN is considered to overhear all of the activities of the Cluster head, as CH is the most compromising node in the complete cluster, and in the case, if the CH is attacked then the complete cluster stops working in the network. The IN is trained based on certain rules and predefined parameters which analyses if the CH or MN is malicious or not and considers the required action. NS2 is considered for the simulation of the proposed methodology and also for the validation of the proposed work. In the proposed methodology, two different stages are considered as detection and correction, which works to tackle the attacks and also considering the system efficiency almost. As in the proposed methodology, the effect of the attack is minimized which increases the QOS and also better data transmission.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Abbreviations

r 0 :

Radius of the cluster

R 0 :

Transmission range of the network

CRV :

Composite reputation range

CH :

Cluster head

IN :

Inspector node

MN :

Member node

node id :

Identity of any node

a :

Constant

Pr id :

Forwarding rate

b :

Constant

E else :

Surplus energy of the node

E 0 :

Initial energy level of the node

References

  1. Gill, R. K., Chawla, P., & Sachdeva, M. (2016). Wireless sensor network: Threat models and security issues.

  2. Sundararaj, V., Muthukumar, S., & Kumar, R. S. (2018). An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Computers and Security, 77, 277–288.

    Article  Google Scholar 

  3. Sundararaj, V. (2016). An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. The International Journal of Intelligent Engineering and Systems, 9(3), 117–126.

    Article  Google Scholar 

  4. Vinu, S. (2019). Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Personal Communications, 104(1), 173–197.

    Article  Google Scholar 

  5. Sundararaj, V. (2019). Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. International Journal of Biomedical Engineering and Technology, 31(4), 325.

    Article  Google Scholar 

  6. Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., et al. (2020). CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Progress in Photovoltaics: Research and Applications, 28(11), 1128–1145.

    Article  Google Scholar 

  7. Rose, S. H., & Jayasree, T. (2019). Detection of jamming attack using timestamp for WSN. Ad Hoc Networks, 91, 101874.

    Article  Google Scholar 

  8. Zhou, H., Shen, S., & Liu, J. (2020). Malware propagation model in wireless sensor networks under attack–defense confrontation. Computer Communications, 162, 51–58.

    Article  Google Scholar 

  9. Tournier, J., Lesueur, F., Le Mouël, F., Guyon, L., & Ben-Hassine, H. (2020). A survey of IoT protocols and their security issues through the lens of a generic IoT stack. Internet of Things, 9, 100264.

    Article  Google Scholar 

  10. Bhushan, B., & Sahoo, G. (2020). ISFC-BLS (intelligent and secured fuzzy clustering algorithm using balanced load sub-cluster formation) in WSN environment. Wireless Personal Communications, 111(3), 1667–1694.

    Article  Google Scholar 

  11. Udhayavani, M., & Chandrasekaran, M. (2018). Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: A trust-aware routing framework for wireless sensor networks. Berlin: Springer.

    Google Scholar 

  12. Krontiris, I., Giannetsos, T., & Dimitriou, T. (2008). Launching a sinkhole attack in wireless sensor networks; the intruder side. In Proceedings of IEEE international conference wireless and mobile computing, networking and communication (WIMOB ’08) (pp. 526–531).

  13. Newsome, J., Shi, E., Song, D., & Perrig, A. (2004). The Sybil attack in sensor networks: Analysis and defences. In Proceedings third international conference information processing in sensor networks (IPSN ’04) (2004).

  14. Bai, L., Ferrese, F., Ploskina, K., & Biswas, S. (2009). Performance analysis of mobile agent-based wireless sensor network. In Proceeding eighth international conference reliability, maintainability and safety (ICRMS ’09) (pp. 16–19).

  15. Zhang, L., Wang, Q., & Shu, X. (2009). A mobile-agent-based middleware for wireless sensor networks data fusion. In Proceeding instrumentation and measurement technology conference (I2MTC ’09) (pp. 378–383).

  16. Xue, W., Aiguo, J., & Sheng, W. (2005). Mobile agent based moving target methods in wireless sensor networks. In Proceedings IEEE international symposium communication and information technology (ISCIT ’05) (vol. 1, pp. 22–26).

  17. Jeong, H.-J., Nam, C.-S., Jeong, Y.-S., & Shin, D.-R. (2008). A mobile agent based leach in wireless sensor networks. In Proceedings 10th international conference advanced communication technology (ICACT ’08) (vol. 1, pp. 75–78).

  18. Al-Karaki, J., & Kamal, A. (2004). Routing techniques in wireless sensor networks: A survey. Wireless Communications, 11(6), 6–28.

    Article  Google Scholar 

  19. Karlof, C., Sastry, N., Wagner, D. (2004). Tinysec: A link layer security architecture for wireless sensor networks. In Proceeding ACM international conference embedded networked sensor systems (SenSys’ 04).

  20. Perrig, A., Szewczyk, R., Wen, W., Culler, D., & Tygar, J. (2002). SPINS: Security protocols for sensor networks. Wireless Networks Journal, 8(5), 521–534.

    Article  Google Scholar 

  21. Watro, R., Kong, D., Cuti, S., Gardiner, C., Lynn, C., & Kruus, P. (2004). Tinypk: Securing sensor networks with public key technology. In Proceedings second ACM workshop security of ad hoc and sensor networks (SASN ’04) (pp. 59–64).

  22. Liu, A., & Ning, P. (2008). Tinyecc: A configurable library for elliptic curve cryptography in wireless sensor networks. In Proceedings seventh international conference information processing in sensor networks (IPSN ’08) (pp. 245–256).

  23. Karlof, C., & Wagner, D. (2003). Secure routing in wireless sensor networks: Attacks and countermeasures in Ad Hoc. Networks, 1(2), 293–315.

    Google Scholar 

  24. Wood, A., & Stankovic, J. A. (2002). Denial of service in sensor networks. IEEE Computer, 35(10), 54–62.

    Article  Google Scholar 

  25. Lee, S. B., & Choi, Y. H. (2006). A resilient packet-forwarding scheme against maliciously packet-dropping nodes in sensor networks. In Proceedings of the fourth ACM workshop on security of ad hoc and sensor networks (SASN’06) (pp. 59–70).

  26. Liu, Y., Dong, M., Ota, K., & Liu, A. (2016). Active trust: Secure and trustable routing in wireless sensor networks. IEEE Transactions on Information Forensics and Security, 11(9), 2013–2027.

    Article  Google Scholar 

  27. Semanti, D., & Abhijit, D. (2015). An algorithm to detect malicious nodes in wireless sensor network using enhanced LEACH protocol. In Proceedings of the 2015 international conference on advances in computer engineering and applications, Ghaziabad, India (pp. 19–20).

  28. Das, S., Barani, S., Wagh, S., & Sonavane, S.S. (2016). Energy efficient and trustable routing protocol for wireless sensor networks based on genetic algorithm (E2TRP). In Proceedings in IEEE international conference on automatic control and dynamic optimization techniques (ICACDOT), Pune, India (pp. 154–159).

  29. Sharmila, S., & Umamaheswari, G. (2011). Detection of sinkhole attack in wireless sensor networks using message digest algorithms. In Proceedings in 2011 international conference on process automation, control and computing, coimbatore, India (pp. 1–6).

  30. Karlof, C., & Wagner, D. (2003). Secure routing in wireless sensor networks: Attacks and countermeasures. In Proceedings in the first IEEE international workshop on sensor network protocols and applications, Anchorage, AK, USA (pp. 113–127).

  31. Alajmi, N. M., & Elleithy, K. (2016). A new approach for detecting and monitoring of selective forwarding attack in wireless sensor networks. In Proceedings in 2016 IEEE long island systems, applications and technology conference (LISAT), Farmingdale, NY, USA (pp. 1–6).

  32. Geethu, P. C., & Mohammed, A. R. (2013). Defense mechanism against selective forwarding attack in wire-less sensor networks. In Proceedings in 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), Tiruchengode, India (pp. 1–4).

  33. Motamedi, M., & Yazdani, N. (2015). Detection of black hole attack in wireless sensor network using uav. In Proceedings in 2015 7th conference on information and knowledge technology (IKT), Urmia, Iran (pp. 1–5).

  34. Latha, D., & Palanivel, K. (2014). Secure routing through trusted nodes in wireless sensor networks a survey. International Journal of Advanced Research in Computer Engineering and Technology, 3(11), 3792–3799.

    Google Scholar 

  35. Mezrag, F., Salim, B., & Mellouk, A. (2017). Secure routing in cluster-based wireless sensor networks. In GLOBECOM 2017–2017 IEEE global communications conference. IEEE.

  36. Jeba, S. V. A., & Suresh Kumar, R. (2015). Reliable anonymous secure packet forwarding scheme for wireless sensor networks. Computers and Electrical Engineering, 48, 405–416.

    Article  Google Scholar 

  37. Sundararajan, R. K., & Arumugam, U. (2015). Intrusion detection algorithm for mitigating sinkhole attack on LEACH protocol in wireless sensor networks. Journal of Sensors.

  38. Chawla, P., & Sachdeva, M. (2018). Detection of selective forwarding (gray hole) attack on LEACH in wireless sensor networks. In Next-generation networks, advances in intelligent systems and computing. Springer Nature Singapore Pte Ltd. (pp. 389–398).

Download references

Acknowledgement

The authors of this paper acknowledge the IK Gujral Punjab Technical University, Kapurthala.

Author information

Authors and Affiliations

  1. Research scholar, IKG PTU Jalandhar, Kapurthala, Punjab, India

    Surinder Singh

  2. Professor, Indo Global College of Engineering, Abhipur, Punjab, India

    Hardeep Singh Saini

Authors
  1. Surinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hardeep Singh Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surinder Singh.

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

Singh, S., Saini, H.S. Learning-Based Security Technique for Selective Forwarding Attack in Clustered WSN. Wireless Pers Commun 118, 789–814 (2021). https://doi.org/10.1007/s11277-020-08044-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-08044-0

Keywords

Search

Navigation