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Estimating Asset Sensitivity by Profiling Users

  • Youngja Park
  • Christopher Gates
  • Stephen C. Gates
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8134)

Abstract

We introduce algorithms to automatically score and rank information technology (IT) assets in an enterprise, such as computer systems or data files, by their business value and criticality to the organization. Typically, information assets are manually assigned classification labels with respect to the confidentiality, integrity and availability. In this paper, we propose semi-automatic machine learning algorithms to automatically estimate the sensitivity of assets by profiling the users. Our methods do not require direct access to the target assets or privileged knowledge about the assets, resulting in a more efficient, scalable and privacy-preserving approach compared with existing data security solutions relying on data content classification. Instead, we rely on external information such as the attributes of the users, their access patterns and other published data content by the users. Validation with a set of 8,500 computers collected from a large company show that all our algorithms perform significantly better than two baseline methods.

Keywords

Asset Sensitivity Criticality Data Security Information Security 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Youngja Park
    • 1
  • Christopher Gates
    • 2
  • Stephen C. Gates
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.Purdue UniversityIndianaUSA

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