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dc.contributor.authorNarayanan, A
dc.contributor.authorChen, Y
dc.contributor.authorPang, S
dc.contributor.authorTao, B
dc.date.accessioned2014-12-08T23:15:10Z
dc.date.available2014-12-08T23:15:10Z
dc.date.copyright2013
dc.identifier.citationThe Scientific World Journal. Volume 2013 (2013), Article ID 671096, 8 pages
dc.identifier.issn1537-744X
dc.identifier.urihttp://hdl.handle.net/10292/8211
dc.description.abstractThe continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka.
dc.languageeng
dc.publisherHindawi Publishing Corporation
dc.relation.urihttp://dx.doi.org/10.1155/2013/671096
dc.rightsCopyright © 2013 Ajit Narayanan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.subjectAmino Acid Sequence
dc.subjectComputer Security
dc.subjectDatabase Management Systems
dc.subjectModels, Theoretical
dc.subjectMolecular Sequence Data
dc.subjectProtein Conformation
dc.subjectProteins
dc.titleThe effects of different representations on static structure analysis of computer malware signatures
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1155/2013/671096
aut.relation.startpage671096
aut.relation.volume2013
pubs.elements-id153636


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