Abstract
There are many issues concerning information security of digital libraries. Apart from traditional information security problems there are some specific ones for digital libraries. In this work we consider a behavioral modeling approach to discover unauthorized copying of a large amount of documents from a digital library. Supposing the regular user has interest in semantically related documents, we treat referencing to semantically unrelated documents as anomalous behavior that may indicate attempt of unauthorized large-scale copying. We use an adapted anomaly detection approach to discover attempts of unauthorized large-scale documents copying. We propose a method for constructing classifiers and profiles of regular users’ behavior based on application of Markov chains. We also present the results of experiments conducted within development of a prototype digital library protection system. Finally, examples of a normal profile and an automatically detected anomalous session derived from the real data logs of a digital library illustrate the suggested approach to the problem.
This work was supported by Kaspersky Lab grant as part of the “Program of support for innovative projects”.
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Ivashko, E.E., Nikitina, N.N. (2012). A Behavioral Modeling Approach to Prevent Unauthorized Large-Scale Documents Copying from Digital Libraries. In: Cao, L., Yu, P. (eds) Behavior Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2969-1_16
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DOI: https://doi.org/10.1007/978-1-4471-2969-1_16
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