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dc.contributor.authorAlneyadi, Sultan
dc.contributor.authorSithirasenan, Elankayer
dc.contributor.authorMuthukkumarasamy, Vallipuram
dc.contributor.editorSusilo, W
dc.contributor.editorMu, Y
dc.date.accessioned2019-02-15T12:32:05Z
dc.date.available2019-02-15T12:32:05Z
dc.date.issued2014
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-08344-5_27
dc.identifier.urihttp://hdl.handle.net/10072/112649
dc.description.abstractData leakage prevention (DLP) is an emerging subject in the field of information security. It deals with tools working under a central policy, which analyze networked environments to detect sensitive data, prevent unauthorized access to it and block channels associated with data leak. This requires special data classification capabilities to distinguish between sensitive and normal data. Not only this task needs prior knowledge of the sensitive data, but also requires knowledge of potentially evolved and unknown data. Most current DLPs use content-based analysis in order to detect sensitive data. This mainly involves the use of regular expressions and data fingerprinting. Although these content analysis techniques are robust in detecting known unmodified data, they usually become ineffective if the sensitive data is not known before or largely modified. In this paper we study the effectiveness of using N-gram based statistical analysis, fostered by the use of stem words, in classifying documents according to their topics. The results are promising with an overall classification accuracy of 92%. Also we discuss classification deterioration when the text is exposed to multiple spins that simulate data modification.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeSwitzerland
dc.relation.ispartofpagefrom413
dc.relation.ispartofpageto421
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume8544
dc.subject.fieldofresearchOther information and computing sciences not elsewhere classified
dc.subject.fieldofresearchcode469999
dc.titleA Semantics-Aware Classification Approach for Data Leakage Prevention
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorMuthukkumarasamy, Vallipuram


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