A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection

This paper introduces a novel Management Traffic Clustering Algorithm (MTCA) based on a sliding window methodology for intrusion detection in 802.11 networks Active attacks and other network events such as scanning, joining and leaving in 802.11 WLANs can be observed by clustering the management fra...

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Detalles Bibliográficos
Autores principales: Zhou, Wenzhe, Marshall, Alan, Gu, Qiang
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Algorithms
Security
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/24100
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-24100
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Algorithms
Security
spellingShingle Ciencias Informáticas
Algorithms
Security
Zhou, Wenzhe
Marshall, Alan
Gu, Qiang
A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
topic_facet Ciencias Informáticas
Algorithms
Security
description This paper introduces a novel Management Traffic Clustering Algorithm (MTCA) based on a sliding window methodology for intrusion detection in 802.11 networks Active attacks and other network events such as scanning, joining and leaving in 802.11 WLANs can be observed by clustering the management frames in the MAC Layer. The new algorithm is based on a sliding window and measures the similarity of management frames within a certain period by calculating their variance. Through filtering out certain management frames, clusters are recognized from the discrete distribution of the variance of the management traffic load. Two parameters determine the accuracy and robustness of the algorithm: the Sample Interval and the Window Size of the sliding window. Extensive tests and comparisons between different sets of Sample Intervals and Window Sizes have been carried out. From analysis of the results, recommendations on what are the most appropriate values for these two parameters in various scenarios are presented.
format Objeto de conferencia
Objeto de conferencia
author Zhou, Wenzhe
Marshall, Alan
Gu, Qiang
author_facet Zhou, Wenzhe
Marshall, Alan
Gu, Qiang
author_sort Zhou, Wenzhe
title A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
title_short A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
title_full A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
title_fullStr A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
title_full_unstemmed A sliding window based management traffic clustering algorithm for 802.11 WLAN intrusion detection
title_sort sliding window based management traffic clustering algorithm for 802.11 wlan intrusion detection
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/24100
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AT zhouwenzhe slidingwindowbasedmanagementtrafficclusteringalgorithmfor80211wlanintrusiondetection
AT marshallalan slidingwindowbasedmanagementtrafficclusteringalgorithmfor80211wlanintrusiondetection
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