Distributed T-Distribution-Based Intrusion Detection in Wireless Sensor Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 295)


Detecting malicious attackers is a critical problem for many sensor network applications. In this paper, a distributed t-distribution-based intrusion detection scheme was proposed. Considering the spatial correlation in the neighborhood activities, our intrusion detection algorithm established a robust model for multiple attributes of sensor nodes using t-distribution. The robust model with an approximate parameter algorithm was exploited to detect malicious attackers precisely. Experimental results show that our algorithm can achieve high detection accuracy and low false alarm rate even when a few sensor nodes are misbehaving, and perform quickly with a lower computational cost.


Wireless sensor networks Intrusion detection t-distribution  Approximate estimation 



This paper was supported by National Science and Technology Major Project of the Ministry of Science and Technology of China. (Grant No. \(2010ZX03006-001-01\)), and National Program on Key Basic Research Project of China. (Grant No. \(2011CB302902\)).


  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y et al (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114Google Scholar
  2. 2.
    Liu F, Cheng X, An F (2006) On the performance of in-situ key establishment schemes for wireless sensor networks. In: IEEE GLOBECOM. IEEE Press, San Francisco, pp 1–5Google Scholar
  3. 3.
    Li GR, He JS, Fu YF (2008) Group-based intrusion detection system in wireless sensor networks. Comput Commun 31(18):4324–4332CrossRefGoogle Scholar
  4. 4.
    Yohai YJ, Zamar R (1988) High breakdown-point estimates of regression by means of the minimization of an efficient scale. J Am Stat Assoc 86(402):403–413Google Scholar
  5. 5.
    Maronna RA, Martin RD, Yohai VJ (2006) Robust statistics: theory and methods. Wiley Publisher, New YorkGoogle Scholar
  6. 6.
    Agah A, Das S, Basu K, Asadi M (2004) Intrusion detection in sensor networks: a non-cooperative game approach. In: The 3rd IEEE international symposium on network computing and applications, pp 343C–346Google Scholar
  7. 7.
    Silva AD, Martin M, Rocha B et al (2005) Decentralized intrusion detection in wireless sensor networks. In: The first ACM international workshop on quality of service and security in wireless and mobile networks, pp 16C–23Google Scholar
  8. 8.
    Su W, Chang K, Kuo Y (2007) eHIP: an energy-efficient hybrid intrusion prohibition system for cluster-based wireless sensor networks. Comput Networks 51(4):1151–C1168CrossRefMATHGoogle Scholar
  9. 9.
    Wang Y, Fu WH, Agrawal DP (2013) Gaussian versus uniform distribution for intrusion detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 24(2):324–355CrossRefMATHGoogle Scholar
  10. 10.
    Krontiris I, Benenson Z, Giannetsos T et al (2009) Cooperative intrusion detection in wireless sensor networks. In: The 6th European conference on wireless sensor networks. Springer, Cork, pp 263–278Google Scholar
  11. 11.
    Liu F, Cheng XZ, Chen D (2007) Insider attacker detection in wireless sensor networks. In: The 26th IEEE international conference on computer communications. IEEE Press, Anchorage, pp 937C–1945Google Scholar
  12. 12.
    Aeschliman C, Park J, Kak AC (2010) A novel parameter estimation algorithm for the multivariate t-distribution and its application to computer vision. In: The 11th European conference on computer vision. Springer, Crete, pp 594–607Google Scholar
  13. 13.
    Chen T, Martin E, Montague G (2009) Robust probabilistic PCA with missing data and contribution analysis for outlier detection. Comput Stat Data Anal 53(10):3706–3716CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Software Engineering InstituteEast China Normal UniversityShangHaiChina
  2. 2.Software SchoolHenan UniversityKaifengChina

Personalised recommendations