Wireless Personal Communications

, Volume 85, Issue 1, pp 207–224 | Cite as

Centralized IDS Based on Misuse Detection for Cluster-Based Wireless Sensors Networks

  • Faouzi Hidoussi
  • Homero Toral-Cruz
  • Djallel Eddine Boubiche
  • Kamaljit Lakhtaria
  • Albena Mihovska
  • Miroslav Voznak


Despite of their energy efficiency, most of WSN’s cluster-based routing protocols are vulnerable to security threats. Selective forwarding and black hole attacks are ranked among the most devastating attacks which they target this class of routing protocols. In this paper, a new centralized intrusion detection system is proposed to detect selective forwarding and black hole attacks in cluster-based wireless sensors networks. The main idea is the use of a centralized detection approach, where the base station decides on potential intrusions based on control packets sent from the cluster heads. The proposed intrusion detection technique is simple and energy efficient, it is thus suitable for sensor nodes with resource constrained. The simulation results confirm the expected performance of the proposed IDS in terms of security and energy efficiency.


Wireless sensor network Cluster-based routing protocols Selective forwarding Black hole Centralized intrusion detection system Misuse detection 


  1. 1.
    Akyildiz, F., et al. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  2. 2.
    Estrin, D., Govindan, R., Heidemann, J. S., & Kumar, S. (1999). Next century challenges: Scalable coordination in sensor networks. In Proceedings of the 5th annual ACM/IEEE international conference on mobile computing and networking, Seattle, WA USA, pp. 263–270.Google Scholar
  3. 3.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000, January). Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii international conference on system sciences, Hawaii, USA, pp. 1–7.Google Scholar
  4. 4.
    Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14–15), 2826–2841.CrossRefGoogle Scholar
  5. 5.
    Lindsey, S., & Raghavendra, C. (2002). PEGASIS: Power-efficient gathering in sensor information systems. Proceedings of the IEEE Aerospace Conference, 3, 1125–1130.Google Scholar
  6. 6.
    Boubiche, D., & Bilami, A. (2011). HEEP (Hybrid Energy Efficiency Protocol) based on chain clustering. International Journal of Sensor Networks, 10(1/2), 25–35.CrossRefGoogle Scholar
  7. 7.
    Karlof, C., & Wagner, D. (2003). Secure routing in wireless sensor networks: Attacks and coun-termeasures. Ad-Hoc Networks, Special Issue on Sensor Network Applications and Protocols, 1(2–3), 293–315.Google Scholar
  8. 8.
    Lee, S., Noh, Y., & Kim, K. (2013). Key schemes for security enhanced TEEN routing protocol in wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, 1–8.Google Scholar
  9. 9.
    Zhang, Y., Lee, W., & Huang, Y. (2003). Intrusion detection techniques for mobile wireless networks. ACM Wireless Networks, 9(5), 545–556.CrossRefGoogle Scholar
  10. 10.
    Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion detection systems in wireless sensor networks: A review. International Journal of Distributed Sensor Networks, 2013, 1–7.Google Scholar
  11. 11.
    Roman, R., Zhou, J., & Lopez, J. (2005, May). On the security of wireless sensor networks. In Proceedings of the ICCSA workshop on internet communications security, Singapur, pp. 681–690.Google Scholar
  12. 12.
    Wood, A. D., & Stankovic, J. A. (2002). Denial of service in sensor networks. Computer, 35(10), 54–62.CrossRefGoogle Scholar
  13. 13.
    Pathan, A. S. K., Lee, H.-W., & Hong, C. S. (2006). Security in wireless sensor networks: Issues and challenges. In Proceedings of the 8th international conference on advanced communication technology (Vol. 2, no. 20–22, pp. 1043–1048). Korea: Phoenix Park.Google Scholar
  14. 14.
    Chen, X. Y., Meng, L. X., & Zhan, Y. Z. (2013). Detecting and defending against replication attacks in wireless sensor networks. International Journal of Distributed Sensor Networks, Volume., 2013, 1–10.Google Scholar
  15. 15.
    Kong, J., Luo, H., Xu, K., Gu, D. L., Gerla, M., & Lu, S. (2002). Adaptive security for multi-layer ad-hoc networks. Special Issue of Wireless Communications and Mobile Computing, New York : Wiley Interscience Press.Google Scholar
  16. 16.
    Albers, P., Camp, O., Percher, J., Jouga, B., Me, L., & Puttini, R. (2002, April). Security in ad hoc networks: A general intrusion detection architecture enhancing trust based approaches. In Proceedings of the first international workshop on wireless information systems, pp. 1–12.Google Scholar
  17. 17.
    Ngai, E. C. H., Liu, J., & Lyu, M. R. (2006, June). On the intruder detection for sinkhole attack in wireless sensor networks. In Proceedings of the IEEE international conference on communications, Istanbul, pp. 3383–3389.Google Scholar
  18. 18.
    Kaplantzis, S., Shilton, A., Mani, N., & AhmetSekercioglu, Y. (2007, December). Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Proceedings of the 3rd international conference on intelligent sensors, sensor networks and information, Melbourne, QLD, pp. 335–340.Google Scholar
  19. 19.
    Hai, T. H., Huh, E. N., & Jo, M. (2010). A lightweight intrusion detection framework for wireless sensor networks. Wireless Communications and Mobile Computing, 10(4), 559–572.Google Scholar
  20. 20.
    Boubiche, D., & Bilami, A. (2012). Cross layer intrusion detection system for wireless sensor network. International Journal of Network Security & Its Applications, 4(2), 35–52.CrossRefGoogle Scholar
  21. 21.
    Sardar, A. R., Sahoo, R. R., Singh, M., Sarkar, S., Singh, J. K., & Majumder, K. (2014). Intelligent intrusion detection system in wireless sensor network. In Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (Vol. 328, pp. 707–712), Advances in Intelligent Systems and Computing.Google Scholar
  22. 22.
    Alrajeh, N. A., & Lloret, J. (2013). Intrusion detection systems based on artificial intelligence techniques in wireless sensor networks. International Journal of Distributed Sensor Networks, 2013, 1–6.Google Scholar
  23. 23.
    Riecker, M., Thies, D., & Hollick, M. (2014). Lightweight detection of denial-of-service attacks in practical wireless sensor networks: A systematic approach. Technical report, Technische Universität Darmstadt, Darmstadt, Germany., January 2014.
  24. 24.
    Jatav, V. K., Tripathi, M., Gaur, M. S., & Laxmi, V. (2012). Wireless sensor networks: Attack models and detection. In Proceedings of the IACSIT Hong Kong conferences IPCSIT (Vol. 30, pp. 144–149). Singapore: IACSIT Press.Google Scholar
  25. 25.
    Pathak, G. R., Patil, S. H., & Tryambake, J. S. (2014). Efficient and trust based black hole attack detection and prevention in WSN. International Journal of Computer Science and Business Informatics, 14(2), 93–103.Google Scholar
  26. 26.
    Sheela, D., Srividhya, V. R., Begam, A., Anjali, & Chidanand G. M. (2012). Detecting black hole attacks in wireless sensor networks using mobile agent. In Proceedings of the international conference on artificial intelligence and embedded systems (pp. 45–48) Singapore, 15–16 July 2012.Google Scholar
  27. 27.
    Dighe, P. G., & Vaidya, M. B. (2013). Deployment of multiple base stations to counter effects of black hole attack on data transmission in wireless sensor network. International Journal of Engineering and Innovative Technology, 3(4), 209–213.Google Scholar
  28. 28.
    Gondwal, N., & Diwaker, C. (2013). Detecting blackhole attack in WSN by check agent using multiple base stations. American International Journal of Research in Science, Technology, Engineering & Mathematics, 13–244, 149–152.Google Scholar
  29. 29.
    Kaur, J., & Kaur, B. (2014). BHDP using fuzzy logic algorithm for wireless sensor network under black hole attack. International Journal of Advance Research in Computer Science and Management Studies, 2(9), 142–151.Google Scholar
  30. 30.
    Da Silva, A. P., Martins, M., Rocha, B., Loureiro, A., Ruiz, L., & Wong, H. C. (2005, October). Decentralized intrusion detection in wireless sensor networks. In: Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile net-works, Montreal, QC, Canada, pp. 16–23.Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Faouzi Hidoussi
    • 1
  • Homero Toral-Cruz
    • 2
  • Djallel Eddine Boubiche
    • 1
  • Kamaljit Lakhtaria
    • 3
  • Albena Mihovska
    • 4
  • Miroslav Voznak
    • 5
  1. 1.Department of Computer ScienceUniversity Hadj Lakhdar of BatnaBatnaAlgeria
  2. 2.Department of Sciences and EngineeringUniversity of Quintana RooChetumalMexico
  3. 3.School of Information TechnologyAuro UniversitySuratIndia
  4. 4.Center for TeleInFrastructurAalborg UniversityAalborgDenmark
  5. 5.Department of TelecommunicationVSB-Technical University of OstravaOstravaCzech Republic

Personalised recommendations