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A graph based framework for malicious insider threat detection

conference contribution
posted on 2017-01-01, 00:00 authored by Anagi GamachchiAnagi Gamachchi, Li Sun, Serdar Boztas
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.

History

Event

Association for Information Systems. Conference (50th : 2017 : Waikoloa Village, Hawaii)

Series

Association for Information Systems Conference

Pagination

2638 - 2647

Publisher

Association for Information Systems

Location

Waikoloa Village, Hawaii

Place of publication

Atlanta, Ga.

Start date

2017-01-04

End date

2017-01-07

ISSN

1530-1605

ISBN-13

978-0-9981331-0-2

Language

eng

Publication classification

E1.1 Full written paper - refereed; E Conference publication

Copyright notice

2017, Association for Information Systems

Editor/Contributor(s)

Tung Bui

Title of proceedings

HICSS : Proceedings of the 50th Hawaii International Conference on System Sciences

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