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dc.contributor.authorAparicio-Navarro, Francisco J.en
dc.contributor.authorChambers, Jonathon A.en
dc.contributor.authorKyriakopoulos, Konstantinosen
dc.contributor.authorGong, Yuen
dc.contributor.authorRixson, Matthewen
dc.contributor.authorBarrington, Stephenen
dc.date.accessioned2019-01-28T14:18:16Z
dc.date.available2019-01-28T14:18:16Z
dc.date.issued2018-02-08
dc.identifier.citationAparicio-Navarro, Francisco J. et al. (2018) Statistical anomaly detection in communication networks. In: The University Defence Research Collaboration In Signal Processing, pp. 124 - 132en
dc.identifier.urihttp://hdl.handle.net/2086/17483
dc.description.abstractThis chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour.en
dc.language.isoenen
dc.publisherDefence Science and Technology Laboratory (Dstl) publication, DSTL/PUB107185.en
dc.subjectIntrusion Detection Systemen
dc.subjectCyber Securityen
dc.subjectNetwork Securityen
dc.titleStatistical anomaly detection in communication networksen
dc.title.alternativeThe University Defence Research Collaboration In Signal Processingen
dc.typeBook chapteren
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.funderDefence Science and Technology Laboratory (Dstl)en
dc.projectidEP/K014307/2en
dc.cclicenceCC BYen
dc.date.acceptance2018-02-08en


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