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Techniques for combining binary classifiers: A comparative study in network intrusion detection systems

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Date Issued:
2004
Summary:
We discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We investigated two different approaches for building the binary classifiers. The results of the binary classifiers are then merged using a combining technique---three different combining techniques were studied. We implement some of the indirect combining techniques proposed in recent literature, and apply them to a case study of the DARPA KDD-1999 network intrusion detection project. The results demonstrate the usefulness of using indirect combining techniques for the multi-category classification problem of intrusion detection systems.
Title: Techniques for combining binary classifiers: A comparative study in network intrusion detection systems.
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Name(s): Lin, Hua.
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: 2004
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 104 p.
Language(s): English
Summary: We discuss a set of indirect combining techniques for addressing multi-category classification problems that have been used in many domains, but not for intrusion detection systems. In contrast to the indirect combining techniques, direct techniques generally extend associated binary classifiers to handle multi-category classification problems. An indirect combining technique decomposes the original multi-category problem into, based on some criteria, multiple binary-category problems. We investigated two different approaches for building the binary classifiers. The results of the binary classifiers are then merged using a combining technique---three different combining techniques were studied. We implement some of the indirect combining techniques proposed in recent literature, and apply them to a case study of the DARPA KDD-1999 network intrusion detection project. The results demonstrate the usefulness of using indirect combining techniques for the multi-category classification problem of intrusion detection systems.
Identifier: 9780496233601 (isbn), 13111 (digitool), FADT13111 (IID), fau:9975 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2004.
Subject(s): Computer networks--Security measures
Computer security
Computers--Access control
Electronic countermeasures
Fuzzy systems
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13111
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.