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User-Assisted Host-Based Detection of Outbound Malware Traffic

Descriptive

Language
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English
Genre (authority = RULIB-FS)
Other
Genre (authority = marcgt)
technical report
PhysicalDescription
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application/pdf
Extent
18 p.
Note (type = special display note)
Technical report DCS-TR-658
Name (type = corporate); (authority = RutgersOrg-School)
NamePart
School of Arts and Sciences (SAS) (New Brunswick)
Name (type = corporate); (authority = RutgersOrg-Department)
NamePart
Computer Science (New Brunswick)
TypeOfResource
Text
TitleInfo
Title
User-Assisted Host-Based Detection of Outbound Malware Traffic
Name (type = personal)
NamePart (type = family)
Xiong
NamePart (type = given)
Huijun
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Malhotra
NamePart (type = given)
Prateek
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Stefan
NamePart (type = given)
Deian
Affiliation
Cooper Union
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Wu
NamePart (type = given)
Chehai
Affiliation
AppFolio, Inc.
Role
RoleTerm (type = text); (authority = marcrt)
author
Name (type = personal)
NamePart (type = family)
Yao
NamePart (type = given)
Danfeng
Affiliation
Computer Science (New Brunswick)
Role
RoleTerm (type = text); (authority = marcrt)
author
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact); (keyDate = yes)
2009-10
Abstract (type = abstract)
Conventional network security solutions are performed on networklayer packets using statistical measures. These types of traffic analysis may not catch stealthy attacks carried out by today’s malware. We aim to develop a host-based security tool that identifies suspicious outbound network connections through analyzing the user’s surfing activities. Specifically, our solution for Web applications predicts user’s network connections by analyzing Web content; unpredicted traffic is further investigated with the user’s help. We describe our method and implementation as well as the experimental results in evaluating its efficiency and effectiveness. We describe how our studies can be applied to detecting bot infection. In order to assess the workload of our host-based traffic-analysis tool, we also perform a large-scale characterization study on 500 university-users’ wireless network traces for 4-month period. We study both the statistical and temporal patterns of individuals’ web usage behaviors from collected wireless network traces. Users are classified into different profiles based on their web usage patterns. Our results show that users have regularities in their Web activities and the expected workload of our traffic-analysis solution is low.
RelatedItem (type = host)
TitleInfo
Title
Computer Science (New Brunswick)
Identifier (type = local)
rucore21032500001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3639T5K
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RightsDeclaration (AUTHORITY = rightsstatements.org); (TYPE = IN COPYRIGHT); (ID = http://rightsstatements.org/vocab/InC/1.0/)
This Item is protected by copyright and/or related rights.You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use.For other uses you need to obtain permission from the rights-holder(s).
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
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Document
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1.4
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MiKTeX pdfTeX-1.40.9
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2009-10-05T19:12:34
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2009-10-05T19:12:34
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