The Journal of Supercomputing

, Volume 75, Issue 4, pp 2221–2242 | Cite as

A comprehensive security analysis of LEACH++ clustering protocol for wireless sensor networks

  • Farrukh Aslam KhanEmail author
  • Ashfaq Hussain Farooqi
  • Abdelouahid Derhab


Wireless sensor networks (WSNs) will play a major role in future technologies in the development of the cyber-physical society. Studies show that WSNs are vulnerable to various insider attacks that may degrade its performance and affect the application services. Various intrusion detection system-based solutions have been proposed for WSNs to secure them from such attacks; however, these solutions have certain limitations with respect to completeness and evaluation. Recently, we proposed an intrusion detection framework to secure WSNs from insider attacks and proposed a protocol called LEACH++. In this paper, we perform a detailed security analysis of LEACH++ against black-hole, sink-hole and selective forwarding attacks by launching a number of attacks with different patterns. The results of our experiments performed in network simulator-2 show that the proposed scheme is highly efficient and achieves higher accuracy and detection rates with very low false-positive rate when compared to an anomaly based detection scheme.


Wireless sensor networks (WSNs) Intrusion detection system (IDS) Low-energy adaptive clustering hierarchy (LEACH) Security analysis 



The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University, Saudi Arabia, for its funding of this research through the Research Group Project No. RGP-214.


  1. 1.
    Jaladi AR, Khithani K, Pawar P, Malvi K, Sahoo G (2017) Environmental monitoring using wireless sensor networks(WSN) based on IoT. Int Res J Eng Technol 4(1):1371–1378Google Scholar
  2. 2.
    Shinde AT, Prasad JR (2017) IoT based animal health monitoring with naive bayes classification. Int Trends Emerg Trends Technol 4(2):8104–8107Google Scholar
  3. 3.
    Botta A, Donato W, Persico V, Pescape A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56:684–700CrossRefGoogle Scholar
  4. 4.
    Aazam M, Huh E, St-Hilaire M, Lung CH, Lambadaris I (2015) Cloud of things: integration of IoT with cloud computing. Robots Sens Clouds 36:77–94CrossRefGoogle Scholar
  5. 5.
    Shanthi S, Rajan EG (2016) Comprehensive analysis of security attacks and intrusion detection system in wireless sensor networks. In: 2nd International Conference on Next Generation Computing Technologies, 2016, pp 426–431Google Scholar
  6. 6.
    Yassen MB, Aljawaerneh S, Abdulraziq R (2016) Secure low energy adaptive clustering hierarchal based on internet of things for wireless sensor network (WSN): survey. In: IEEE International Conference on Engineering & MIS, Agadir, Morocco, 2016, pp 1–9Google Scholar
  7. 7.
    Imran M, Khan FA, Abbas H (2014) Detection and Prevention of black hole attacks in mobile ad hoc networks. In: Security in Ad Hoc Networks (SecAN) Workshop, 13th International Conference on Ad-Hoc and Wireless Networks (Ad Hoc Now 2014), Benidorm, Spain, June 22–27, 2014Google Scholar
  8. 8.
    Ho CY et al (2012) False positives and negatives from real traffic with intrusion detection/prevention systems. Int J Future Comput Commun 1(2):87–90CrossRefGoogle Scholar
  9. 9.
    Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  10. 10.
    WB Heinzelman, AP Chandrakasan, H Balakrishnan (2000) Energy-efficient routing protocols for wireless microsensor networks. In: 33rd Hawaii International Conference System Sciences, Maui, HI, 2000Google Scholar
  11. 11.
    Tripathi M, Laxmi V (2013) Comparing the impact of black hole and gray hole attack on LEACH in WSN. Procedia Comput Sci 19(1):1101–1107CrossRefGoogle Scholar
  12. 12.
    Karlof C, Wagner D (2003) Secure routing in wireless sensor networks: attacks and countermeasures. In: The 1st IEEE International Workshop on Sensor Network Protocols and Applications, 2003, pp 113–127Google Scholar
  13. 13.
    Ferreira AC et al (2005) On the security of cluster-based communication protocols for wireless sensor networks. In: 4th IEEE International Conference on Networking, 2005, pp 449–458Google Scholar
  14. 14.
    Zhang K, Wang C, Wang C (2008) A secure routing protocol for cluster-based wireless sensor networks using group key management. In: 4th IEEE International Conference on Wireless Communications, Networking and Mobile Computing, 2008, pp 1–5Google Scholar
  15. 15.
    Wu D, Hu G, Ni G (2008) Research and improve on secure routing protocols in wireless sensor networks. In: 4th IEEE International Conference on Circuits and Systems for Communications, 2008, pp 853–856Google Scholar
  16. 16.
    Su CC, Chang KM, Kuo YH, Horng MF (2005) The new intrusion prevention and detection approaches for clustering-based sensor networks. In: IEEE Wireless Communications and Networking Conference, 2005, pp 1927–1932Google Scholar
  17. 17.
    Lee S, Lee Y, Yoo SG (2012) A specification based intrusion detection mechanism for the LEACH protocol. J Inf Technol 11(1):40–48CrossRefGoogle Scholar
  18. 18.
    Kumar SR, Umamakeswari A (2016) SSLEACH: Specification based secure LEACH protocol for wireless sensor networks. In: IEEE WiSPNET, 2016, pp 1672–1676Google Scholar
  19. 19.
    Almomani I, Al-Kasasbeh B, Al-Akhras M (2016) WSN-DS: a dataset for intrusion detection systems in wireless sensor networks. J Sens 2016:1–16CrossRefGoogle Scholar
  20. 20.
    Farooqi AH, Khan FA (2012) A survey of intrusion detection systems for wireless sensor networks. Int J Ad Hoc Ubiquit Comput 9(2):69–83CrossRefGoogle Scholar
  21. 21.
    Farooqi AH, Khan FA, Wang J, Lee S (2013) A novel intrusion detection framework for wireless sensor networks. Pers Ubiquit Comput 17(5):907–919CrossRefGoogle Scholar
  22. 22.
    Farooqi AH, Khan FA (2017) Securing wireless sensor networks for improved performance in cloud-based environments. Ann Telecommun 72(5):265–282CrossRefGoogle Scholar
  23. 23.
    Masdari M, Bazarchi SM, Bidaki M (2013) Analysis of secure LEACH-BASED clustering protocols in wireless sensor networks. J Netw Comput Appl 36(2013):1243–1260CrossRefGoogle Scholar
  24. 24.
    Sundararajan RK, Arumugam U (2015) Intrusion detection algorithm for mitigating sinkhole attack on LEACH protocol in wireless sensor networks. J Sens 2015:1–12CrossRefGoogle Scholar
  25. 25.
    Bansal V, Saluja KK (2016) Anomaly based detection of black hole attack on LEACH protocol in WSN. In: IEEE International conference on Wireless Communications, Singal processing and Networking, Chennai, India, 2016, pp 1924–1928Google Scholar
  26. 26.
    Mcdermott CD, Petrovki A (2017) Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks. Int J Comput Netw Commun 9(4):45–56Google Scholar
  27. 27.
    Rizwan R, Khan FA, Abbas H, Chauhdary SH (2015) Anomaly detection in wireless sensor networks using immune-based bio-inspired mechanism. Int J Distrib Sens Netw 2015:Article ID 684952Google Scholar
  28. 28.
    Da Silva APR et al (2005) Decentralized intrusion detection in wireless sensor networks. In: Proceedings of the 1st ACM International Workshop on Quality of Service & Security in Wireless and Mobile Networks, Quebec, Canada, 2005, pp 16–23Google Scholar
  29. 29.
    Roman R, Zhou J, Lopez J (2006) Applying intrusion detection systems to wireless sensor networks. In: 3rd IEEE Consumer Communications and Networking Conference, 2006, pp 640–644Google Scholar
  30. 30.
    Krontiris I, Dimitriou T (2007) Towards intrusion detection in wireless sensor networks. In: 13th European Wireless Conference, Paris, 2007Google Scholar
  31. 31.
    Krontiris I, Dimitriou T, Giannetsos T (2008) LIDeA: a distributed lightweight intrusion detection architecture for sensor networks. In: ACM Secure Communication, Istanbol, TurkeyGoogle Scholar
  32. 32.
    Marchang N, Datta R (2008) Collaborative techniques for intrusion detection in mobile ad-hoc networks. Ad Hoc Netw VI:508–523CrossRefGoogle Scholar
  33. 33.
    Khan FA, Imran M, Abbas H, Durad MH (2017) A detection and prevention system against collaborative attacks in mobile ad hoc networks. Future Gener Comput Syst (Elsevier) 68:416–427CrossRefGoogle Scholar
  34. 34.
    Otoum S, Kantarci B, Hussein TM (2017) Mitigating false negative intruder decisions in WSN-based smart grid monitoring. In: IEEE 13th International Wireless Communications and Mobile Computing Conference, 2017, pp 153–158Google Scholar
  35. 35.
    Bahl S, Sharma SK (2015) Improving classification accuracy of intrusion detection system using feature subset selection. In: IEEE 5th International Conference on Advanced Computing & Communication Technologies, 2015, pp 431–436Google Scholar
  36. 36.
    Milenkoski A, Jayaram KR, Antunes N, Vieira M, Kounev S (2016) Quantifying the attack detection accuracy of intrusion detection systems in virtualized environments. In: IEEE 27th International Symposium on Software Reliability Engineering, 2016, pp 276–286Google Scholar
  37. 37.
    Jabbar MA, Aluvalu R, Sai Reddy S (2017) Cluster based ensemble classification for intrusion detection system. In: ACM 9th International Conference on Machine Learning and Computing, 2017, pp 253–257Google Scholar
  38. 38.
    Deng H, Li W, Agrawal Dharma P (2002) Routing security in ad hoc networks. IEEE Commun Mag Spec Top Sec Telecommun Netw 40(10):70–75Google Scholar
  39. 39.
    Al-Shurman M, Yoo SM, Park S (2004) Black hole attack in mobile ad hoc networks. In: 42nd Annual Southeast Regional Conference ACM-SE, 2004, pp 96–97Google Scholar
  40. 40.
    Ghugar U, Pradhan J (2016) A study on black hole attack in wireless sensor networks. In: National Conference on Next Generation Computing and Its Applications in Science & Technology, IGIT, Sarang, 2016, pp 1–3Google Scholar
  41. 41.
    Javaid A, Niyaz Q, Sun W, Alam M (2015) A deep learning approach for network intrusion detection system. In: 9th EAI International Conference on Bio-inspired Information and Communications Technologies, New York City, United States, 2015, pp 21–26Google Scholar
  42. 42.
    Krontiris I, Giannetsos T, Dimitriou T (2008) Launching a Sinkhole attack in wireless sensor networks; the intruder side. In: International Conference on Wireless and Mobile Computing Networking and Communications, 2008, pp 526–531Google Scholar
  43. 43.
    Agrwal SL, Khandelwal R, Sharma P, Gupta SK (2016) Analysis of detection algorithm of Sinkhole attack & QoS on AODV for MANET. In: 2nd International Conference on Next Generation Computing Technologies, Dehradun, India, 2016, pp 839–842Google Scholar

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

Authors and Affiliations

  1. 1.Center of Excellence in Information AssuranceKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer ScienceNational University of Computer and Emerging SciencesIslamabadPakistan
  3. 3.Department of Computer ScienceCOMSATS University IslamabadIslamabadPakistan

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