Open Access

Multilayer Statistical Intrusion Detection in Wireless Networks

  • Mohamed Hamdi1Email author,
  • Amel Meddeb-Makhlouf1 and
  • Noureddine Boudriga1
EURASIP Journal on Advances in Signal Processing20082009:368589

https://doi.org/10.1155/2009/368589

Received: 6 September 2007

Accepted: 16 September 2008

Published: 18 November 2008

Abstract

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.

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Authors’ Affiliations

(1)
Communication Networks and Security Research Laboratory, School of Communication Engineering, University of 7th of November at Carthage

Copyright

© 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.