Crime data mining: a general framework and some examples

A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. Data mining is a powerful tool that enables criminal investigators who may lack extensive training as data analysts to explore large databases quickly and efficiently. We present a general framework for crime data mining that draws on experience gained with the Coplink project, which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997.

[1]  Stephen G. MacDonell,et al.  Software Forensics: Extending Authorship Analysis Techniques to Computer Programs , 2002 .

[2]  Salvatore J. Stolfo,et al.  A data mining framework for building intrusion detection models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[3]  Hsinchun Chen,et al.  An International Perspective on Fighting Cybercrime , 2003, ISI.

[4]  Ramasamy Uthurusamy,et al.  EVOLVING DATA MINING INTO SOLUTIONS FOR INSIGHTS , 2002 .

[5]  George M. Mohay,et al.  Mining e-mail content for author identification forensics , 2001, SGMD.

[6]  Ted E. Senator,et al.  The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions , 1995, IAAI.

[7]  Kun Liu,et al.  Privacy Sensitive Distributed Data Mining from Multi-party Data , 2003, ISI.

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  Hsinchun Chen,et al.  Extracting Meaningful Entities from Police Narrative Reports , 2002, DG.O.

[10]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[11]  Hsinchun Chen,et al.  Using Coplink to Analyze Criminal-Justice Data , 2002, Computer.

[12]  Ramasamy Uthurusamy,et al.  Evolving data into mining solutions for insights , 2002, CACM.

[13]  Gang Wang,et al.  Automatically detecting deceptive criminal identities , 2004, CACM.

[14]  Ted E. Senator,et al.  The Financial Crimes Enforcement Network AI System (FAIS) Identifying Potential Money Laundering from Reports of Large Cash Transactions , 1995, AI Mag..