research-article

On the efficacy of data mining for security applications

Author:

Ted E. SenatorAuthors Info & Claims

CSI-KDD '09: Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics

Pages 75 - 83

https://doi.org/10.1145/1599272.1599286

Published: 28 June 2009 Publication History

Abstract

Data mining applications for security have been proposed, developed, used, and criticized frequently in the recent past. This paper examines several of the more common criticisms and analyzes some factors that bear on whether the criticisms are valid and/or can be overcome by appropriate design and use of the data mining application.

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Cited By

Balaji JMin CJaved FZhu Y(2018)AvatarProceedings of the Second Workshop on Data Management for End-To-End Machine Learning10.1145/3209889.3209892(1-10)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1145/3209889.3209892
Young WMemory AGoldberg HSenator T(2014)Detecting Unknown Insider Threat ScenariosProceedings of the 2014 IEEE Security and Privacy Workshops10.1109/SPW.2014.42(277-288)Online publication date: 17-May-2014
https://dl.acm.org/doi/10.1109/SPW.2014.42
Shen QBoongoen T(2012)Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2010.25524:4(649-664)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1109/TKDE.2010.255

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Published In

cover image ACM Conferences

CSI-KDD '09: Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics

June 2009

94 pages

ISBN:9781605586694

DOI:10.1145/1599272

Editors:
Hsinchun Chen
The University of Arizona, Tucson
,
Marc Dacier
Symantec Research Labs Europe, France
,
Marie-Francine Moens
K.U. Leuven, Belgium
,
Gerhard Paass
Fraunhofer IAIS, St. Augustin, Germany
,
Christopher C. Yang
Drexel University, Philadelphia

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28, 2009

Paris, France

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Cited By

Balaji JMin CJaved FZhu Y(2018)AvatarProceedings of the Second Workshop on Data Management for End-To-End Machine Learning10.1145/3209889.3209892(1-10)Online publication date: 15-Jun-2018
https://dl.acm.org/doi/10.1145/3209889.3209892
Young WMemory AGoldberg HSenator T(2014)Detecting Unknown Insider Threat ScenariosProceedings of the 2014 IEEE Security and Privacy Workshops10.1109/SPW.2014.42(277-288)Online publication date: 17-May-2014
https://dl.acm.org/doi/10.1109/SPW.2014.42
Shen QBoongoen T(2012)Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2010.25524:4(649-664)Online publication date: 1-Apr-2012
https://dl.acm.org/doi/10.1109/TKDE.2010.255

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