On off-topic access detection in information systems

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On off-topic access detection in information systems

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Conference on Information and Knowledge Management archive
Proceedings of the 14th ACM international conference on Information and knowledge management table of contents

Bremen, Germany

POSTER SESSION: Poster Session table of contents

Pages: 353 - 354

Year of Publication: 2005

ISBN:1-59593-140-6

Authors

Nazli Goharian	Illinois Institute of Technology
Ling Ma	Illinois Institute of Technology

Sponsors

ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 4, Downloads (12 Months): 44, Citation Count: 2

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abstract references cited by index terms collaborative colleagues

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ABSTRACT

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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	B. Aleman-Meza, et al: An Ontological Approach to the Document Access Problem of Insider Threat. IEEE Intelligence and Security Informatics (ISI), 2005.
	2	Rebecca Cathey , Ling Ma , Nazli Goharian , David Grossman, Misuse detection for information retrieval systems, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA [doi>10.1145/956863.956901]
	3	N. Goharian, et al: Detecting Misuse of Information Retrieval Systems Using Data Mining Techniques. IEEE Intelligence and Security Informatics (ISI), 2005.
	4	Ling Ma , Nazli Goharian, Query length impact on misuse detection in information retrieval systems, Proceedings of the 2005 ACM symposium on Applied computing, March 13-17, 2005, Santa Fe, New Mexico [doi>10.1145/1066677.1066921]
	5	S. Symonenko, et al: Semantic Analysis for Monitoring Insider Threats. IEEE Intelligence and Security Informatics (ISI), 2004.
	6	O. Yilmazel, et al: Leveraging One-Class SVM and Semantic Analysis to Detect Anomalous Content. IEEE Intelligence and Security Informatics (ISI), 2005.

CITED BY 2

	Alana Platt , Nazli Goharian , Saket S. R. Mengle, On using user query sequence to detect off-topic search, Proceedings of the 2007 ACM symposium on Applied computing, March 11-15, 2007, Seoul, Korea
	Alana Platt , Saket S. R. Mengle , Nazli Goharian, Improving classification based off-topic search detection via category relationships, Proceedings of the 2009 ACM symposium on Applied Computing, March 08-12, 2009, Honolulu, Hawaii