Graph Based Framework for Malicious Insider Threat Detection

dc.contributor.author Gamachchi, Anagi
dc.contributor.author Sun, Li
dc.contributor.author Boztas, Serdar
dc.date.accessioned 2016-12-29T01:02:32Z
dc.date.available 2016-12-29T01:02:32Z
dc.date.issued 2017-01-04
dc.description.abstract While 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.319
dc.identifier.isbn 978-0-9981331-0-2
dc.identifier.uri http://hdl.handle.net/10125/41475
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Anomaly Detection
dc.subject Behavioural Analysis
dc.subject Graph Analysis
dc.subject Information Security
dc.subject Insider Threat
dc.title Graph Based Framework for Malicious Insider Threat Detection
dc.type Conference Paper
dc.type.dcmi Text
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