A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Role templates for content-based access control
RBAC '97 Proceedings of the second ACM workshop on Role-based access control
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward cost-sensitive modeling for intrusion detection and response
Journal of Computer Security
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
An ontological approach to the document access problem of insider threat
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Improving classification based off-topic search detection via category relationships
Proceedings of the 2009 ACM symposium on Applied Computing
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In many organizations, it is common to control access to confidential information based on the need-to-know principle; The requests for access are authorized only if the content of the requested information is relevant to the requester’s current information analysis project. We formulate such content-based authorization, i.e. whether to accept or reject access requests as a binary classification problem. In contrast to the conventional error-minimizing classification, we handle this problem in a cost-sensitive learning framework in which the cost caused by incorrect decision is different according to the relative importance of the requested information. In particular, the cost (i.e., damaging effect) for a false positive (i.e., accepting an illegitimate request) is 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. From the comparison of the cost-sensitive classifiers with error-minimizing classifiers, we found that the costing with a 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.