Graph Based Framework for Malicious Insider Threat Detection

dc.contributor.authorGamachchi, Anagi
dc.contributor.authorSun, Li
dc.contributor.authorBoztas, Serdar
dc.date.accessioned2016-12-29T01:02:32Z
dc.date.available2016-12-29T01:02:32Z
dc.date.issued2017-01-04
dc.description.abstractWhile most security projects have focused on fending off attacks coming from outside the organizational boundaries, a real threat has arisen from the people who are inside those perimeter protections. \ Insider threats have shown their power by hugely affecting national security, financial stability, and the privacy of many thousands of people. What is in the news is the tip of the iceberg, with much more going on under the radar, and some threats never being detected. We propose a hybrid framework based on graphical analysis and anomaly detection approaches, to combat this severe cyber security threat. Our framework analyzes heterogeneous data in isolating possible malicious users hiding behind others. Empirical results reveal this framework to be effective in distinguishing the majority of users who demonstrate typical behavior from the minority of users who show suspicious behavior. \
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.319
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41475
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnomaly Detection
dc.subjectBehavioural Analysis
dc.subjectGraph Analysis
dc.subjectInformation Security
dc.subjectInsider Threat
dc.titleGraph Based Framework for Malicious Insider Threat Detection
dc.typeConference Paper
dc.type.dcmiText

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