Insider threat simulation and performance analysis of insider detection algorithms with role based models
Nellikar, Suraj
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https://hdl.handle.net/2142/16177
Description
Title
Insider threat simulation and performance analysis of insider detection algorithms with role based models
Author(s)
Nellikar, Suraj
Issue Date
2010-05-19T18:39:53Z
Director of Research (if dissertation) or Advisor (if thesis)
Nicol, David M.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Insider threat
Insider detection algorithms
Role-based access control
Abstract
Insider threat problems are widespread in industry today. They have resulted in huge
losses to organizations. The security reports by leading organizations point out the fact that there
have been many more insider attacks in recent years than any other form of attack. Detection of
these insider threats is a top priority. One problem facing the detection mechanisms is that the
real data for modeling is not easily available. This thesis describes a simulator which can
simulate the insiders and generate access information in the form of logs.
Currently there are many methods which use data mining algorithms to detect insider
attacks. Role based detection is a well known mechanism to accurately distinguish insider
behavior from the normal behavior. The thesis focuses on the advantages of using role based
mechanisms for insider threat detection. Five algorithms have been chosen and performance
analysis of these under various scenarios is carried out. The thesis discusses these results in
detail.
The simulator is built on the Scalable Simulation Framework (SSF). It is an extension of
the Boeing simulator, JANUS. The simulator uses behavior files to model an insider/normal user
and generates the access information using Markov chains.
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