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Addressing Insider Threat through Cost-Sensitive Document Classification

  • Young-Woo Seo
  • Katia Sycara
Part of the Integrated Series In Information Systems book series (ISIS, volume 18)

Most organizations use computerized security systems to manage and protect their confidential information. While security is mostly concerned with prevention of attacks from outsiders, security breaches by insiders have recently gained increasing attention from the security community. In this chapter, we describe a cost-sensitive document classification scheme which forms the basis for determining the legitimacy of confidential access by insiders. Our scheme enforces compliance with the “need to know” security principle, namely that the requests for access are authorized only if the content of the requested information is relevant to the requester’s current information analysis project. First, we formulate such content-based authorization, i.e., whether to accept or reject access requests as a binary classification problem. Second, we implement this problem in a costsensitive learning framework in which the cost caused by incorrect decision is different according to the relative importance of the error types; false positive and false negative. In particular, the cost for a false positive (i.e., accepting a security violating request) is considered more expensive than that of false negative (i.e., rejecting a valid request). The former is a serious security problem because confidential information, which should not be revealed, can be accessed. We experimentally compared various costsensitive classifiers with conventional error-minimizing classifiers. Our results indicate that costing using logistic regression showed the best performance, in terms of the smallest cost paid, the lowest false positive rate, and the relatively low false negative rate.

Keywords

Confidential Information Document Classification Misclassification Cost Rejection Sampling Insider Threat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Young-Woo Seo
    • 1
  • Katia Sycara
    • 2
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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