Abstract
To present game theory approach to detect identity crimes. Improve the adaptability of identity crime detection systems to real time application. Time constraints on the reactive time of the detection and fraud events need to be minimized. Identity crime has major thrust in credit application. Existing work presented multilayered detection system based on two layers named as Communal detection and Spike detection. Dynamic Time Warping algorithm is applied to minimize the time constraints on detecting fraudulent identity usage and reaction time. Performance analysis is carried out on CD and SD with real credit applications. Experiment is conducted on real time credit card application using UCI repository data sets with synthetic and real data sets. Keywords - Data mining-based fraud detection, security, and data stream mining, and anomaly detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
