Random resampling algorithms for addressing the imbalanced dataset classes in insider threat detecti...
Random resampling algorithms for addressing the imbalanced dataset classes in insider threat detection
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Cybersecurity threats can be perpetrated by insiders or outsiders. The threats that could be carried out by insiders are far more serious due to their privileged access, which they may use to cause financial loss and reputation harm for an organization. Thus, insider threats represent a major cybersecurity challenge for private and government organ...
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Full title
Random resampling algorithms for addressing the imbalanced dataset classes in insider threat detection
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2819140110
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2819140110
Other Identifiers
ISSN
1615-5262
E-ISSN
1615-5270
DOI
10.1007/s10207-022-00651-1
