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Multilayer Statistical Intrusion Detection in Wireless Networks


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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Correspondence to Mohamed Hamdi.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hamdi, M., Meddeb-Makhlouf, A. & Boudriga, N. Multilayer Statistical Intrusion Detection in Wireless Networks. EURASIP J. Adv. Signal Process. 2009, 368589 (2008).

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