An Efficient Intrusion Detection Scheme for Wireless Sensor Networks

  • Chunming Rong
  • Skjalg Eggen
  • Hongbing Cheng
Part of the Communications in Computer and Information Science book series (CCIS, volume 187)


As a hot issue, wireless sensor network have gained widely attention. WSNs in general and in nature are unattended and physically reachable from the outside world, they could be vulnerable to physical attacks in the form of node capture or node destruction. These forms of attacks are hard to protect against and require intelligent prevention methods. It is necessary for WSNs to have security measures in place as to prevent an intruder from inserting compromised nodes in order to decimate or disturb the network performance. In this paper we present an intrusion detection algorithm for wireless sensor networks which does not require prior knowledge of network behavior or a learning period in order to establish this knowledge. We have taken a more practical approach and constructed this algorithm with small to middle-size networks in mind, like home or office networks. The proposed algorithm is also dynamic in nature as to cope with new and unknown attack types. This algorithm is intended to protect the network and ensure reliable and accurate aggregated sensor readings. Theoretical simulation results in three different scenarios indicate that compromised nodes can be detected with high accuracy and low false alarm probability.


Wireless Sensor Network Intrusion Detection Security 


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  1. 1.
    Maronna, R.A., Martin, R.D., Yohai, V.J.: Robust Statistics: Theory and Methods, vol. ch. 6.9.1, pp. 205–208. Wiley Publisher, Chichester (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Hussain, S., Mohamed, M.A., Holder, R., Almasri, A., Shukur, G.: Performance evaluation based on the robust mahluationbis distance and multilevel modelling using two new strategies (February 2008)Google Scholar
  3. 3.
    Filzmoser, P.: A multiivariate outlier detection methodGoogle Scholar
  4. 4.
    Alberts, P., Kuhn, M.: Security in ad hoc networks: A general intrusion detection architecture enhancing trust based approaches. In: First International Workshop on Wireless Information Systems, 4th International Conference on Enterprise Information Systems (2002)Google Scholar
  5. 5.
    Liu, F., Cheng, X., Chen, D.: Insider Attacker Detection in Wirelss Sensor Networks. In: IEEE Proceedings INFOCOM 2007 (2007)Google Scholar
  6. 6.
    Alqallaf, F.A., Konis, K.P., Douglas Martin, R., Zamar, R.H.: Scalable robust covariance and correlation estimates for data mining. In: ACM SIGKDD 2002, Edmonton, Alberta, Canada, pp. 14–23 (2002)Google Scholar
  7. 7.
    Ngai E.C.H.: Intrusion Detection for Wireless Sensor Networks. Ph.D. – Term 2 Paper (2005)Google Scholar
  8. 8.
    Sarma, H.K.D., Manipal, S., Kar, A.: Security Threats in Wireless Sensor Networks (2006)Google Scholar
  9. 9.
    Newsome, J., Shi, E., Song, D., Perrig, A.: The Sybil Attack in Sensor Networks: Analysis & Defenses (2004)Google Scholar
  10. 10.
    Staniford-Chen, S., Cheng, S., Crawford, R., Dilger, M.: GRIDS – A Graph Based Intrusion Detection System for Large Networks. In: The 19th National Information Systems Security Conference (1996)Google Scholar
  11. 11.
    Brutch, P., Ko, C.: Challenges in intrusion detection for wireless sensor networks. In: Proceedings 2003 Symposium on Applications and the Internet Workshops, pp. 368–373 (2003)Google Scholar
  12. 12.
    Lancaster, H.O.: The chi-squared distribution. Wiley, Chichester (1969)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chunming Rong
    • 1
  • Skjalg Eggen
    • 2
  • Hongbing Cheng
    • 1
    • 3
  1. 1.Department of Electronic Engineering & Computer ScienceUniversity of StavangerStavangerNorway
  2. 2.Department of InformaticsOslo UniversityBlindernNorway
  3. 3.College of ComputerNanjing University of Posts&TelecommunicationsNanjingChina

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