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A higher order collective classifier
Descriptive
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Text
TitleInfo
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Title
A higher order collective classifier
SubTitle
PartName
PartNumber
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A
Identifier
ETD_1557
Identifier
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051380
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eng
Genre
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theses
Subject
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Topic
Computer Science
Subject
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(authority = ETD-LCSH)
Topic
Computer networks--Security measures
Abstract
Modern statistical machine learning techniques often rely on the assumption that data instances are independent and identically distributed (IID). However, recent work in statistical relational learning has demonstrated the utility of violating the independence assumption. Specifically, the research has shown the value of leveraging relationships between data instances based on higher-order paths. In this thesis, I present a novel Higher Order Collective Classifier (HOCC), a statistical relational machine learning technique that leverages latent information present in higher-order co-occurrences of items across data instances. A general framework is presented in which HOCC can be applied to event detection in time series data. Given the importance of cyber-security, HOCC is applied to two different data sets in the cyber-security domain: first, a Border Gateway Protocol (BGP) dataset, for detection and classification of anomalies, and second, a Network File System dataset for building models of user activity for masquerade detection. Performance of HOCC compares favorably against first-order models that do not leverage higher-order information, achieving separation of classes that heretofore were difficult to separate.
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electronic resource
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ix, 63 p. : ill.
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Note
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M.S.
Note
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Includes bibliographical references (p. 60-63)
Note
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by Vikas Menon
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Menon
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Vikas
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Vikas Menon
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Kantor
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Paul
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chair
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Paul B Kantor
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Liviu
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Advisory Committee
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Liviu Iftode
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Marian
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Am?lie
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Advisory Committee
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Am?lie Marian
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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2009
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2009-05
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xx
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NjNbRU
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Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
Identifier
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doi:10.7282/T35D8S27
Genre
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ETD graduate
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The author owns the copyright to this work.
Copyright
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Copyright protected
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Open
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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