NDLI: Role-based differentiation for insider detection algorithms

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Role-based differentiation for insider detection algorithms

Role-based differentiation for insider detection algorithms

Content Provider	ACM Digital Library
Author	Choi, Jai J. Nicol, David M. Nellikar, Suraj
Abstract	Insider threat problems are widespread in industry today, resulting in large losses of intellectual property. Reputable reports assert that attacks from within an organization are on the rise, making detection of insider-based attacks a top priority. This paper evaluates the effectiveness of using role-based differentiation of user behavior as a tool in detecting insider attack behavior. This differentiation is natural in contexts where role-based access control (RBAC) mechanisms are in place. Using synthetically generated traffic (which puts placement and intensity of insider behavior under experimental control), we train five different algorithms on "normal" behavior with and without RBAC differentiation, and measure the accuracy of detecting malicious behavior with, and without RBAC, as a function of insider behavior. We find that in some contexts RBAC differentiation significantly reduces these errors. However, in our experiments two of the five algorithms had statistically significant increases in false positives under RBAC as opposed to non-RBAC. However, these increases are small compared to the very large gain in detection capability that RBAC brings, and we conclude that RBAC is very much worth considering as a tool for insider threat detection.
Starting Page	55
Ending Page	62
Page Count	8
File Format	PDF
ISBN	9781450300926
DOI	10.1145/1866886.1866897
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Insider threat Data mining algorithms Role based access Insider detection algorithms
Content Type	Text
Resource Type	Article

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