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Anomaly detection in network using non-negative matrix factorization techniques

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TitleInfo
Title
Anomaly detection in network using non-negative matrix factorization techniques
Name (type = personal)
NamePart (type = family)
Kang
NamePart (type = given)
Yunyi
NamePart (type = date)
1991-
DisplayForm
Yunyi Kang
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong Kee
DisplayForm
Myong Kee Jeong
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong
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Myong Jeong
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Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Li
NamePart (type = given)
Kang
DisplayForm
Kang Li
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Wang
NamePart (type = given)
Honggang
DisplayForm
Honggang Wang
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Anomaly detection is becoming an important problem in graph mining. This is because people are eager to find out unusual objects or patterns in a network, which may results in possible damages, emerging trends, or even creations in different types of graphs or networks. For example, the transaction occurs in out-of- hometown area with high amount may indicate credit card fraud in a bank transaction network; a substantial high frequency of connectivity in the network may infer possible web attack. The discoveries of these activities are important for people’s life and personal information security, and thus are important tasks for relevant institutions. There are mainly three kinds of anomalies in a graph data: node, edge and subgraph anomaly. This thesis presents new graph anomaly detection algorithms that provide scoring functions on detecting both node anomalies and edge anomalies based on non-negative matrix factorization techniques. The experiments on real life data verify that the suggested method could provide better performance in finding meaningful anomalies.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6430
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vii, 55 p. : ill.)
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Graph theory
Subject (authority = ETD-LCSH)
Topic
Anomalies
Subject (authority = ETD-LCSH)
Topic
Data mining
Note (type = statement of responsibility)
by Yunyi Kang
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T33R0VV5
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kang
GivenName
Yunyi
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-04-15 23:58:12
AssociatedEntity
Name
Yunyi Kang
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
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ETD
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windows xp
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