Adaptive communal detection in search of adversarial identity crime Phua, Clifton, Lee, Vincent, Smith-Miles, Kate and Gayler, Ross 2007, Adaptive communal detection in search of adversarial identity crime, in Proceedings of the 2007 International Workshop on Domain Driven Data Mining : August 12, 2007, San Jose, California, ACM, New York, N.Y, pp. 1-10. Attached Files Name Description MIMEType Size Downloads Title Adaptive communal detection in search of adversarial identity crime Author(s) Phua, Clifton Lee, Vincent Smith-Miles, Kate Gayler, Ross Conference name International Workshop on Domain Driven Data Mining (2007 : San Jose, California) Conference location San Jose, California Conference dates 12-15 August 2007 Title of proceedings Proceedings of the 2007 International Workshop on Domain Driven Data Mining : August 12, 2007, San Jose, California Editor(s) Yu, Philips Publication date 2007 Conference series International Workshop on Domain Driven Data Mining Start page 1 End page 10 Publisher ACM Place of publication New York, N.Y Keyword(s) adaptive fraud/communal detection adversarial identity crime automated credit application stream processing system Summary This paper is on adaptive real-time searching of credit application data streams for identity crime with many search parameters. Specifically, we concentrated on handling our domain-specific adversarial activity problem with the adaptive Communal Analysis Suspicion Scoring (CASS) algorithm. CASS's main novel theoretical contribution is in the formulation of State-of- Alert (SoA) which sets the condition of reduced, same, or heightened watchfulness; and Parameter-of-Change (PoC) which improves detection ability with pre-defined parameter values for each SoA. With pre-configured SoA policy and PoC strategy, CASS determines when, what, and how much to adapt its search parameters to ongoing adversarial activity. The above approach is validated with three sets of experiments, where each experiment is conducted on several million real credit applications and measured with three appropriate performance metrics. Significant improvements are achieved over previous work, with the discovery of some practical insights of adaptivity into our domain. ISBN 9781595938466 159593846X Language eng Field of Research 080499 Data Format not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2007 ACM Persistent URL http://hdl.handle.net/10536/DRO/DU:30008098 Document type: Conference Paper Collection: School of Engineering and Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 20 Jan 2009, 12:52:24 EST Thu, 26 Aug 2010, 15:06:00 EST Fri, 16 Mar 2012, 16:25:44 EST Wed, 21 Oct 2015, 13:21:03 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 621 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 29 Sep 2008, 09:04:31 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.