Online fraud detection system based on non-stationery anomaly detection

Proceedings of The 2005 International Conference on Security and Management, SAM'05

Volumn , Issue , 2005, Pages 364-370

  Xu, Jianyun ^a   Sung, Andrew H ^a   Liu, Qingzhong ^a  

^a NEW MEXICO INSTITUTE OF MINING AND TECHNOLOGY   (United States)

Author keywords

Anomaly detection; Fraud detection; Non stationery data

Indexed keywords

ANOMALY DETECTION; AUTOMATIC DESIGNS; CUSTOMER BEHAVIORS; FRAUD DETECTION; FRAUD DETECTION SYSTEMS; NON-STATIONERY DATA; TRAINING TECHNIQUES; TRANSACTION DATABASE; USER PROFILES; USER PROFILING;

CUSTOMER SATISFACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

LAWS AND LEGISLATION;

EID: 62349095053     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. Of the 20th Intl. Conf. on Very Large Data Bases (Santiago, Chile, September 1994), pp. 478-499.
2. R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. of the International Conference on Data Engineering (Taipei, Taiwan, March 1995), pp. 3-14.
3. R. Meo, G. Psaila, and S. Ceri. A new SQL-like operator for mining association rules. In Proc. of the 22nd Intl. Conf. on Very Large Data Bases (Mumbai, India, September 1996), pp. 122-133.
4. R. Ng, L. Lakshmanan, J. Han, and AND A. Pang. Exploratory mining and pruning optimizations of constrained association rules. In Proc. of the ACM SIGMOD Intl. Conf. on Management of Data (Seattle, Washington, June 1998), pp. 13-24.
5. D. Cheung, J. Han, V. Ng and C. Wang. Maintenance of discovered association rules in large databases: an incremental updating technique. In Proc. of the Intl. Conf. on Data Engineering (New Orleans, Louisiana, USA, February 1996), pp. 106-114.
6. R. Agrawal and J. Shafer. Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering 8, 6, 962-969.
7. S. Brin, R. Motwani and C. Silverstein. Beyond market basket: generalizing association rules to correlations. In Proc. of the ACM SIGMOD Intl. Conf. on Management of Data (Tucson, Arizona, USA, June 1997), pp. 265-276.
8. B. Ozden, A. Ramaswamy and A. Silberchatz. Cyclic association rules. In Proc. Of the Intl. Conf. on Data Engineering (1998), pp. 412-421
9. J. Han, J. Pei and Y. Yin. Mining frequent patterns without candidate generation. SIGMOD'00, 1-12, Dallas, TX, May 2000
10. S. Clearwater and F.Provost. RL4: A tool for knowledge-based induction. In Proc. Of the Second International IEEE Conference on Tools for Artificial Intelligence, pp. 24-30.
11. J. R. Quinlan. C4.5: Program for machine learning. Morgan Kaufmann, San Mateo, CA, 1993.
12. K. Kerr and A. Litan. Online Transaction Fraud and Prevention Get More Sophisticated, Gartner, 2002. http://www.gartnerg2.com/research/rpt-0102-0013. asp
13. C. Elkan. Naïve Bayesian Learning. Technical Report CS97-557, Department of Computer Science and Engineering, University of California, San Diego, USA, 1997.
14. th Conference on Machine Learning, Bari, Italy, pp105-112, 1996.
15. C. Elkan, Magical thinking in data mining: lessons from CoIL challenge 2000, Department of Computer Science and Engineering, University of California, San Diego, USA, 2000.
16. J. Han and M. Kamber. Data Mining: Concepts and Technique, Morgan Kaufmann Publishers, San Francisco, USA, 2001
17. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java, Morgan Kauffman Publishers, California, USA, 1999.
18. P. Chan and S. Stolfo. Toward scalable learning with non-uniform class and cost distributions: A case study in credit card fraud detection. Proc. of the Fourth International Conference on Knowledge Discovery and Data Mining, 164-168.
19. M. Chung, N.J. Puketza, R.A. Olsson and B. Mukherjee. Simulating concurrent intrusions for testing intrusion detection systems: parallelizing intrusions. Proc. of the 1995 National Information Systems Security Conference, 173-183.