File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A Generic Bayesian Belief Model for Similar Cyber Crimes

TitleA Generic Bayesian Belief Model for Similar Cyber Crimes
Authors
Tse, KSHChow, KPKwan, YK
KeywordsBayesian networks
BitTorrent file sharing
DDoS attacks
Issue Date2013
PublisherSpringer New York LLC. The Journal's web site is located at http://www.springer.com/series/6102
Citation
The 9th Annual IFIP WG 11.9 International Conference on Digital Forensics, Florida, USA, 27-30 January 2013. In IFIP Advances in Information and Communication Technology, 2013, v. 410, p. 243-255 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-642-41148-9_17
AbstractBayesian belief network models designed for specific cyber crimes can be used to quickly collect and identify suspicious data that warrants further investigation. While Bayesian belief models tailored to individual cases exist, there has been no consideration of generalized case modeling. This paper examines the generalizability of two case-specific Bayesian belief networks for use in similar cases. Although the results are not conclusive, the changes in the degrees of belief support the hypothesis that generic Bayesian network models can enhance investigations of similar cyber crimes.
DescriptionIFIP Advances in Information and Communication Technology, vol. 410 entitled: Advances in digital forensics IX: 9th IFIP WG 11.9 International Conference on Digital Forensics, Orlando, FL, USA, January 28-30, 2013, Revised selected papers
Persistent Identifierhttp://hdl.handle.net/10722/203654
ISBN
9783642411472
ISSN
1868-4238
2023 SCImago Journal Rankings: 0.242

 

DC FieldValueLanguage
dc.contributor.authorTse, KSHen_US
dc.contributor.authorChow, KPen_US
dc.contributor.authorKwan, YKen_US
dc.date.accessioned2014-09-19T15:49:10Z-
dc.date.available2014-09-19T15:49:10Z-
dc.date.issued2013en_US
dc.identifier.citationThe 9th Annual IFIP WG 11.9 International Conference on Digital Forensics, Florida, USA, 27-30 January 2013. In IFIP Advances in Information and Communication Technology, 2013, v. 410, p. 243-255en_US
dc.identifier.isbn9783642411472-
dc.identifier.issn1868-4238-
dc.identifier.urihttp://hdl.handle.net/10722/203654-
dc.descriptionIFIP Advances in Information and Communication Technology, vol. 410 entitled: Advances in digital forensics IX: 9th IFIP WG 11.9 International Conference on Digital Forensics, Orlando, FL, USA, January 28-30, 2013, Revised selected papers-
dc.description.abstractBayesian belief network models designed for specific cyber crimes can be used to quickly collect and identify suspicious data that warrants further investigation. While Bayesian belief models tailored to individual cases exist, there has been no consideration of generalized case modeling. This paper examines the generalizability of two case-specific Bayesian belief networks for use in similar cases. Although the results are not conclusive, the changes in the degrees of belief support the hypothesis that generic Bayesian network models can enhance investigations of similar cyber crimes.-
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://www.springer.com/series/6102en_US
dc.relation.ispartofIFIP Advances in Information and Communication Technologyen_US
dc.rightsThe original publication is available at www.springerlink.comen_US
dc.subjectBayesian networks-
dc.subjectBitTorrent file sharing-
dc.subjectDDoS attacks-
dc.titleA Generic Bayesian Belief Model for Similar Cyber Crimesen_US
dc.typeConference_Paperen_US
dc.identifier.emailChow, KP: chow@cs.hku.hken_US
dc.identifier.authorityChow, KP=rp00111en_US
dc.identifier.doi10.1007/978-3-642-41148-9_17en_US
dc.identifier.scopuseid_2-s2.0-84886470438-
dc.identifier.hkuros240111en_US
dc.identifier.volume410en_US
dc.identifier.spage243en_US
dc.identifier.epage255en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.issnl1868-4238-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats