Distributed anomaly detection, using cooperative learners and association rule analysis

Intelligent Data Analysis

Volumn 12, Issue 4, 2008, Pages 339-357

  Deshmeh, G ^a   Rahmati, M ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Association rule analysis; Credit card fraud detection; Distributed anomaly detection; Feature selection

Indexed keywords

EID: 51149094760     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2008-12403     Document Type: Article

References (34)

1. G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999.
2. H. Kargupta, A. Joshi, K. Sivakumar and Y. Yesha, Data Mining: Next Generation Challenges and Future Directions, Prentice Hall of India (2005), 157-219.
3. S.J. Stolfo, A.L. Prodromidis, S. Tselepis, D.W. Fan and P.K. Chan, JAM: Java Agents for meta-learning over distributed databases, KDD 97 (1997), 74-81.
4. H. Kargupta, I. Hamzaoglu, B. Stafford, V Hanagandi and K. Buescher, PADMA: Parallel Data Mining Agents for Scalable Text Classification, Proceedings of the High Performance Computing, 1997.
5. A. Schuster and R. Wolff, Communication-Efficient Distributed Mining of Association Rules, Data Mining and Knowledge Discovery 8(2) (2004).
6. S. Bailey, R. Grossman, H. Sivakumar and A. Turinsky, Papyrus: A System for Data Mining Over Local and Wide Area Clusters and Super-clusters, Proceedings of the 1999 ACM/IEEE conference on Supercomputing, 1999, 63-66.
7. J. Bala, S. Baik, A. Hadjarian, B.K. Gogia and C. Manthorne, Application of a Distributed Data Mining Approach to Network Intrusion Detection, Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, Bologna, Italy, 2002, 1419-1420.
8. S. Stolfo, W. Fan, W. Lee, A. Prodromidis and P. Chan, Cost-based Modeling for Fraud and Intrusion Detection: Results from the JAM Project Proceedings of the 2000 DARPA Information Survivability Conference and Exposition (DISCEX '00), 2000.
9. X. Hang and H. Dai, Applying both Positive and Negative Selection to Supervised Learning for Anomaly Detection, Proceedings of the GECCO'05 conference, 2005, 345-352.
10. , Journal of Artificial Intelligence Research 16 (2002), 321-357.
11. C.C. Noble and D.J. Cook, Graph-Based Anomaly Detection, SIGKDD03, 2003, 631-636.
12. K. Yamanishi, J. Takeuchi and G. Williams, Online Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms, KDD2000, 2000, 320-324.
13. Lazarevic and V Kumar, Feature Bagging for Outlier Detection, Proceedings of the conference on knowledge discovery from data KDD'05, 2005, 157-166.
14. T. Mitchel, Machine Learning, McGraw-Hill, 1997, 230-245.
15. C. Elkan, The foundations of cost-sensitive learning, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001, 973-978.
16. A. Estabrooks and N. Japkowicz, A mixture-of-experts framework for learning from unbalanced data sets, Proceedings of the 2001 Intelligent Data Analysis Conference, 2001, pp. 34-43.
17. M. Halkidi, Y. Batistakis and M. Vazirgiannis, On Clustering Validation Techniques, Journal of Intelligent Systems 17(2/3) (2001), 107-145.
18. N.V Chawla, Editorial: Special Issue on Learning from Imbalanced Data Sets, Sigkdd Explorations 6(Issue 1) (2004), 1-6.
19. S.J. Stolfo, W. Lee., P.K. Chan, W. Fan and E. Eskin, Data Mining-based Intrusion Detectors: An Overview of the Columbia IDS Project, SIGMOD Record 30(4) (2001), 5-14.
20. G. Tandon, P. Chan and D. Mitra, MORPHEUS: Motif Oriented Representations to Purge Hostile Events from Unlabeled Sequences, Proceedings of the DMSEC'04 conference, 2004, 16-25.
21. S.D. Bay and M. Schwabacher, Mining Distance Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule, SIGKDD '03 (2003), 29-38.
22. G.M. Weiss, Mining with Rarity: A Unifying Framework, SIGKDD Explorations 6(Issue 1) (2004), 7-19.
23. C. Phua, D. Alahakoon and V Lee, Minority Report in Fraud Detection: Classification of Skewed Data, SIGKDD Explorations 6(1) (2004), 50-59.
24. K. Sequeira and M. Zaki, ADMIT: Anomaly-based Data Milling for Intrusions, SIGKDD 02 (2002), 386-395.
25. P. Chan and S. Stolfo, Toward Scalable Learning with Nonurliform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, Calif., 1998, 164-168.
26. C. Chang and S. Chiang, Anomaly Detection and Classification for Hyperspectral Imagery, IEEE Transactions on Geosciences and Remote Sensing 40(6) (2002), 1314-1325.
27. I. Steinwart, D. Hush and C. Scovel, A Classification Framework for Anomaly Detection, Journal of Machine Learning Research 6 (2005), 211 232.
28. N.V Chawla, L.O. Hall and A. Joshi, Wrapper-based computation and evaluation of sampling methods for imbalanced datasets. In Proceedings of the 1st international Workshop on Utility-Based Data Mining (Chicago, Illinois, August 21-21, 2005). UBDM '05. ACM Press, New York, NY, 24-33.
29. http://www.cs.waikato.ac.nz/m1/weka/.
30. http://mill.ucsd.edu/index.php?page=Datasets.
31. E. Eskin, Detecting errors within a corpus using anomaly detection. In Proceedings of the First Conference on North American Chapter of the Association for Computational Linguistics (Seattle, Washington, April 29 - May 04, 2000). ACM International Conference Proceeding Series, vol. 4. Morgan Kaufmann Publishers, San Francisco, CA, 148-153.
32. 2 multivariate profiling for anomaly detection, Proceedings of the First IEEE SMC Information Assurance and Security Workshop, 2000, 175-181.
33. E. Otey, A. Ghoting and S. Parthasarathy, Fast Distributed Outlier Detection in Mixed-Attribute Data Sets, Data Mining and Knowledge Discovery 12(Issue 2-3) (2006), 203 228.
34. http://ww.jade.tilab.com/.