Misuse detection for information retrieval systems
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Query length impact on misuse detection in information retrieval systems
Proceedings of the 2005 ACM symposium on Applied computing
Leveraging one-class SVM and semantic analysis to detect anomalous content
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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
Detecting misuse of information retrieval systems using data mining techniques
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
On using user query sequence to detect off-topic search
Proceedings of the 2007 ACM symposium on Applied computing
Improving classification based off-topic search detection via category relationships
Proceedings of the 2009 ACM symposium on Applied Computing
Hyperclique pattern based off-topic detection
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Detection using clustering query results
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Detection of anomalies from user profiles generated from system logs
AISC '11 Proceedings of the Ninth Australasian Information Security Conference - Volume 116
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We focus on detecting insider access violations to off-topic documents. Previously, we utilized information retrieval techniques, e.g., clustering and relevance feedback, to warn of potential misuse. For the relevance feedback approach, we minimize the indicative features needed for detection using data mining techniques. We show that the derived reduced feature subset achieves equivalent performance to that of the previously derived full set of features.