Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41475

Graph Based Framework for Malicious Insider Threat Detection

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Title: Graph Based Framework for Malicious Insider Threat Detection
Authors: Gamachchi, Anagi
Sun, Li
Boztas, Serdar
Keywords: Anomaly Detection
Behavioural Analysis
Graph Analysis
Information Security
Insider Threat
Issue Date: 04 Jan 2017
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. \
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41475
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.319
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Inside the Insider Threat Minitrack



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