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  • Research Article
  • Open Access

Multilayer Statistical Intrusion Detection in Wireless Networks

  • 1Email author,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20082009:368589

  • Received: 6 September 2007
  • Accepted: 16 September 2008
  • Published:


The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs). This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The architecture is adequate to wireless networks because the underlying detection models rely on radio parameters and traffic models. Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS. A radio signal fingerprinting technique based on the maximal overlap discrete wavelet transform (MODWT) is developed. Moreover, a geometric clustering algorithm is presented. Depending on the characteristics of the fingerprinting technique, the clustering algorithm permits to control the false positive and false negative rates. Finally, simulation experiments have been carried out to validate the proposed IDS.


  • Cluster Algorithm
  • Wireless Network
  • False Negative Rate
  • Intrusion Detection
  • Discrete Wavelet

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Authors’ Affiliations

Communication Networks and Security Research Laboratory, School of Communication Engineering, University of 7th of November at Carthage, 2083 Ariana, Tunisia


© Mohamed Hamdi et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.