International Journal of Information Technology

, Volume 10, Issue 4, pp 489–494 | Cite as

PL-IDS: physical layer trust based intrusion detection system for wireless sensor networks

  • Umashankar Ghugar
  • Jayaram Pradhan
  • Sourav Kumar Bhoi
  • Rashmi Ranjan Sahoo
  • Sanjaya Kumar Panda
Original Research


In this paper, a physical layer trust based intrusion detection system (PL-IDS) is proposed to calculate the trust for wireless sensor networks (WSNs) at the physical layer. The trust value of sensor node is calculated as per the deviation of key factors at the physical layer. The proposed scheme is effective to identify the abnormal nodes in WSNs. The abnormal nodes mainly attack the physical layer by denial of service attack. They use the jamming attack by consuming the resources of the genuine nodes, which leads to a denial of service. To analyze the performance of PL-IDS, we have implemented the periodic jamming attack. Results show that PL-IDS performs better in terms of false alarm rate and malicious node detection accuracy rate.


Physical layer protocol Trust Intrusion detection system Wireless sensor networks 


  1. 1.
    Estrin D, Govindan R, Heidemann J, Kumar S (1999) Next century challenges: scalable coordination in sensor networks. In: The 5th annual ACM/IEEE international conference on mobile computing and networking, ACM/IEEE, pp 263–270Google Scholar
  2. 2.
    Bhasin V, Kumar S, Saxena P, Katti C (2018) Security architectures in wireless sensor network. Int J Inf Process. Google Scholar
  3. 3.
    Mehra P, Doja M, Alam B (2017) Zonal based approach for clustering in heterogeneous WSN. Int J Inf Process. Google Scholar
  4. 4.
    Mehta R, Lobiyal D (2017) Utility-based performance analysis of cross-layer design in multi-flow ad-hoc networks. Int J Inf Process Springer 9(4):377–387Google Scholar
  5. 5.
    Aggarwal M, Nilay K, Yadav K (2017) Survey of named data networks: future of internet. Int J Inf Process Springer 9(2):197–207Google Scholar
  6. 6.
    Sun L, Li J, Chen Y, Zhu H (2005) Wireless sensor network. Tsinghua University Press, BeijingGoogle Scholar
  7. 7.
    Bao F, Chen I, Chang M, Cho J (2012) Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. IEEE Trans Netw Serv Manage 9(2):169–183CrossRefGoogle Scholar
  8. 8.
    Wood A, Stankovic J (2002) Denial of service in sensor networks. Comput IEEE 35(10):54–62CrossRefGoogle Scholar
  9. 9.
    Depren O, Topallar M, Anarim E, Ciliz M (2005) An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst Appl Elsevier 29(4):713–722CrossRefGoogle Scholar
  10. 10.
    Azhagiri M, Rajesh A (2018) A novel approach to measure the quality of cluster and finding intrusions using intrusion unearthing and probability clomp algorithm. Int J Inf Process. Google Scholar
  11. 11.
    Muttoo S, Badhani S (2017) Android malware detection: state of the art. Int J Inf Process Springer 9(1):111–117Google Scholar
  12. 12.
    Feng R, Xu X, Zhou X, Wan J (2011) A trust evaluation algorithm for wireless sensor networks based on node behaviors and D-S evidence theory. Sensors 11(2):1345–1360CrossRefGoogle Scholar
  13. 13.
    Wu R, Deng X, Lu R, Shen X (2012) Trust-based anomaly detection in wireless sensor networks. In: 1st IEEE international conference on communications in China, IEEE, pp 203–207Google Scholar
  14. 14.
    Atakli I, Hu H, Chen, Y, Ku W, Su Z (2008) Malicious node detection in wireless sensor networks using weighted trust evaluation. In: Proceedings of the 2008 spring simulation multi conference, ACM, pp 836–843Google Scholar
  15. 15.
    Panda S, Jana P (2015) A multi-objective task scheduling algorithm for heterogeneous multi-cloud environment. In: International conference on electronic design, computer networks and automated verification, IEEE, pp 82–87Google Scholar
  16. 16.
    Panda S, Jana P (2017) An efficient request-based virtual machine placement algorithm for cloud computing. In: 13th international conference on distributed computing and internet technology, Springer, pp 129–143Google Scholar
  17. 17.
    Panda S, Jana P (2016) An efficient task consolidation algorithm for cloud computing. In: 12th international conference on distributed computing and internet technology, Springer, pp 61–74Google Scholar
  18. 18.
    Panda S, Jana P (2017) SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput Springer 73(6):2730–2762CrossRefGoogle Scholar
  19. 19.
    Wang J, Jiang S, Fapojuwo A (2017) A protocol layer trust-based intrusion detection scheme for wireless sensor networks. Sensors 17(6):12–27Google Scholar
  20. 20.
    Panda S, Jana P (2014) An efficient energy saving task consolidation algorithm for cloud computing. In: 3rd IEEE international conference on parallel, distributed and grid computing, IEEE, pp 262–267Google Scholar
  21. 21.
    Panigrahi P, Panda S, Tripathy C (2015) Energy efficient task consolidation algorithms for cloud computing systems. Int J Inf Process 9(4):34–45Google Scholar
  22. 22.
    Rout J, Bhoi S, Panda S (2013) SFTP: a secure and fault-tolerant paradigm against blackhole attack in MANET. Int J Comput Appl 64(4):27–32Google Scholar
  23. 23.
    Rezazadeh J, Moradi M, Ismail A, Dutkiewicz E (2015) Impact of static trajectories on localization in wireless sensor networks. Wireless Netw Springer 21(3):809–827CrossRefGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceBerhampur UniversityBerhampurIndia
  2. 2.Department of Computer Science and EngineeringParala Maharaja Engineering CollegeBerhampurIndia
  3. 3.Department of Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia

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