Identifying dependency between secure messages for protocol analysis

Chen, Q; Zhang, S; Chen, YPP

Identifying dependency between secure messages for protocol analysis

Chen, Q Zhang, S Chen, YPP

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4798 LNAI pp. 30 - 38
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Chen, Q	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Chen, YPP	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4798 LNAI pp. 30 - 38	en_US
dc.identifier.isbn	9783540767183	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1996
dc.description.abstract	Collusion attack has been recognized as a key issue in e-commerce systems and increasingly attracted people's attention for quite some time in the literatures of information security. Regardless of the wide application of security protocol, this attack has been largely ignored in the protocol analysis. There is a lack of efficient and intuitive approaches to identify this attack since it is usually hidden and uneasy to find. Thus, this article addresses this critical issue using a compact and intuitive Bayesian network (BN)-based scheme. It assists in not only discovering the secure messages that may lead to the attack but also providing the degree of dependency to measure the occurrence of collusion attack. The experimental results demonstrate that our approaches are useful to detect the collusion attack in secure messages and enhance the protocol analysis. © Springer-Verlag Berlin Heidelberg 2007.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Identifying dependency between secure messages for protocol analysis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4798 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melboure	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	4798 LNAI	en_US

Abstract:

Collusion attack has been recognized as a key issue in e-commerce systems and increasingly attracted people's attention for quite some time in the literatures of information security. Regardless of the wide application of security protocol, this attack has been largely ignored in the protocol analysis. There is a lack of efficient and intuitive approaches to identify this attack since it is usually hidden and uneasy to find. Thus, this article addresses this critical issue using a compact and intuitive Bayesian network (BN)-based scheme. It assists in not only discovering the secure messages that may lead to the attack but also providing the degree of dependency to measure the occurrence of collusion attack. The experimental results demonstrate that our approaches are useful to detect the collusion attack in secure messages and enhance the protocol analysis. © Springer-Verlag Berlin Heidelberg 2007.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/1996