NDLI: Leveraging social networks to detect anomalous insider actions in collaborative environments

Content Provider	IEEE Xplore Digital Library
Author	You Chen Nyemba, S. Wen Zhang Malin, B.
Copyright Year	2011
Description	Author affiliation: Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN 37203 USA (You Chen; Nyemba, S.; Malin, B.) \|\| Department of Electrical Engineering and Computer Science, School of Engineering, Vanderbilt University, Nashville, TN 37203 USA (Wen Zhang)
Abstract	Collaborative information systems (CIS) enable users to coordinate efficiently over shared tasks. They are often deployed in complex dynamic systems that provide users with broad access privileges, but also leave the system vulnerable to various attacks. Techniques to detect threats originating from beyond the system are relatively mature, but methods to detect insider threats are still evolving. A promising class of insider threat detection models for CIS focus on the communities that manifest between users based on the usage of common subjects in the system. However, current methods detect only when a user's aggregate behavior is intruding, not when specific actions have deviated from expectation. In this paper, we introduce a method called specialized network anomaly detection (SNAD) to detect such events. SNAD assembles the community of users that access a particular subject and assesses if similarities of the community with and without a certain user are sufficiently different. We present a theoretical basis and perform an extensive empirical evaluation with the access logs of two distinct environments: those of a large electronic health record system (6,015 users, 130,457 patients and 1,327,500 accesses) and the editing logs of Wikipedia (2,388,955 revisors, 55,200 articles and 6,482,780 revisions). We compare SNAD with several competing methods and demonstrate it is significantly more effective: on average it achieves 20–30% greater area under an ROC curve.
Starting Page	119
Ending Page	124
File Size	1622208
Page Count	6
File Format	PDF
ISBN	9781457700828
e-ISBN	9781457700859
DOI	10.1109/ISI.2011.5984061
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electronic publishing Analytical models Atmospheric measurements Intrusion detection Medical services Automatic voltage control
Content Type	Text
Resource Type	Article

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