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Intrusion detection in wireless networks: A data mining approach

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Date Issued:
2005
Summary:
The security of wireless networks has gained considerable importance due to the rapid proliferation of wireless communications. While computer network heuristics and rules are being used to control and monitor the security of Wireless Local Area Networks (WLANs), mining and learning behaviors of network users can provide a deeper level of security analysis. The objective and contribution of this thesis is three fold: exploring the security vulnerabilities of the IEEE 802.11 standard for wireless networks; extracting features or metrics, from a security point of view, for modeling network traffic in a WLAN; and proposing a data mining-based approach to intrusion detection in WLANs. A clustering- and expert-based approach to intrusion detection in a wireless network is presented in this thesis. The case study data is obtained from a real-word WLAN and contains over one million records. Given the clusters of network traffic records, a distance-based heuristic measure is proposed for labeling clusters as either normal or intrusive. The empirical results demonstrate the promise of the proposed approach, laying the groundwork for a clustering-based framework for intrusion detection in computer networks.
Title: Intrusion detection in wireless networks: A data mining approach.
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Name(s): Nath, Shyam Varan.
Florida Atlantic University, Degree grantor
Khoshgoftaar, Taghi M., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2005
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 90 p.
Language(s): English
Summary: The security of wireless networks has gained considerable importance due to the rapid proliferation of wireless communications. While computer network heuristics and rules are being used to control and monitor the security of Wireless Local Area Networks (WLANs), mining and learning behaviors of network users can provide a deeper level of security analysis. The objective and contribution of this thesis is three fold: exploring the security vulnerabilities of the IEEE 802.11 standard for wireless networks; extracting features or metrics, from a security point of view, for modeling network traffic in a WLAN; and proposing a data mining-based approach to intrusion detection in WLANs. A clustering- and expert-based approach to intrusion detection in a wireless network is presented in this thesis. The case study data is obtained from a real-word WLAN and contains over one million records. Given the clusters of network traffic records, a distance-based heuristic measure is proposed for labeling clusters as either normal or intrusive. The empirical results demonstrate the promise of the proposed approach, laying the groundwork for a clustering-based framework for intrusion detection in computer networks.
Identifier: 9780542127205 (isbn), 13246 (digitool), FADT13246 (IID), fau:10101 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2005.
Subject(s): Wireless communication systems
Data warehousing
Data mining
Telecommunication--Security measures
Computer networks--Security measures
Computer security
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13246
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.