Abstract
This paper introduces a novel method of tracking user computer behavior to create highly granular profiles of usage patterns. These profiles, then, are used to detect deviations in a users’ online behavior, detecting intrusions, malicious insiders, misallocation of resources, and out-of-band business processes. Successful detection of these behaviors significantly reduces the risk of leaking sensitive data, or inadvertently exposing critical assets.
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Robinson, D.J., Berk, V.H., Cybenko, G.V. (2008). Online Behavioral Analysis and Modeling Methodology (OBAMM). In: Liu, H., Salerno, J.J., Young, M.J. (eds) Social Computing, Behavioral Modeling, and Prediction. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77672-9_12
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DOI: https://doi.org/10.1007/978-0-387-77672-9_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-77671-2
Online ISBN: 978-0-387-77672-9
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