Investigative Data Mining for Security and Criminal Detection

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April Learn how and when to remove this template message. Main articles: Machine learning and Data mining. Main article: Supervised learning. Main article: Unsupervised learning.

Retrieved 28 August February Multilogic in Science. Retrieved February 2, Retrieved Sift Science. Archived from the original on Journal of Digital Forensics, Security and Law. University of Texas at Dallas. Statistical Science 17 3 : — Unsupervised Profiling Methods for Fraud Detection. Bratko, and M.

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Kubat Auditing 16 1 : 14— Held, and C. Perez Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems with Applications 31, — Technical Report WS AAAI Press. Signature-Based Methods for Data Streams. Data Mining and Knowledge Discovery 5: — Journal of Parallel and Distributed Computing. Data Mining and Knowledge Discovery. Unsupervised Profiling for Identifying Superimposed Fraud.

Proceedings of PKDD' Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications 10 Hand, Statistical fraud detection: A review. Statistical Science 17 3 , pp. ACL, Categories : Fraud Data analysis. Hidden categories: Pages with reference errors Pages with broken reference names CS1 maint: archived copy as title Wikipedia articles with style issues from April All articles with style issues CS1 maint: multiple names: authors list.

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State Web Sites. Forensic Resources and Information Web Sites. Appendix C: Intrusion Detection Glossary. The FBI director went on to tell Congress that the bureau would shift its focus from reacting to crime to preventing it, acknowledging that this could be done only with better technology, which, again, is what this book is about, specifically: Data integration for access to multiple and diverse sources of information Link analysis for visualizing criminal and terrorist associations and relations Software agents for monitoring, retrieving, analyzing, and acting on information Text mining for sorting through terabytes of documents, Web pages, and e-mails Neural networks for predicting the probability of crimes and new terrorist attacks Machine-learning algorithms for extracting profiles of perpetrators and graphical maps of crimes This book strives to explain the technologies and their applications in plain English, staying clear of the math, and instead concentrating on how they work and how they can be used by law enforcement investigators, Featured Products.

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Investigative Data Mining for Security and Criminal Detection

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