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 prot...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2008-12-01
|
Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2009/368589 |
id |
doaj-d9de081f3d6e424990b78a69156b0f31 |
---|---|
record_format |
Article |
spelling |
doaj-d9de081f3d6e424990b78a69156b0f312020-11-24T23:27:18ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802008-12-01200910.1155/2009/368589Multilayer Statistical Intrusion Detection in Wireless NetworksNoureddine BoudrigaAmel Meddeb-MakhloufMohamed HamdiThe 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.http://dx.doi.org/10.1155/2009/368589 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Noureddine Boudriga Amel Meddeb-Makhlouf Mohamed Hamdi |
spellingShingle |
Noureddine Boudriga Amel Meddeb-Makhlouf Mohamed Hamdi Multilayer Statistical Intrusion Detection in Wireless Networks EURASIP Journal on Advances in Signal Processing |
author_facet |
Noureddine Boudriga Amel Meddeb-Makhlouf Mohamed Hamdi |
author_sort |
Noureddine Boudriga |
title |
Multilayer Statistical Intrusion Detection in Wireless Networks |
title_short |
Multilayer Statistical Intrusion Detection in Wireless Networks |
title_full |
Multilayer Statistical Intrusion Detection in Wireless Networks |
title_fullStr |
Multilayer Statistical Intrusion Detection in Wireless Networks |
title_full_unstemmed |
Multilayer Statistical Intrusion Detection in Wireless Networks |
title_sort |
multilayer statistical intrusion detection in wireless networks |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2008-12-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2009/368589 |
work_keys_str_mv |
AT noureddineboudriga multilayerstatisticalintrusiondetectioninwirelessnetworks AT amelmeddebmakhlouf multilayerstatisticalintrusiondetectioninwirelessnetworks AT mohamedhamdi multilayerstatisticalintrusiondetectioninwirelessnetworks |
_version_ |
1725552460109447168 |