Anomaly detection using an ensemble of feature models

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 953-958

  Noto, Keith ^a   Brodley, Carla ^a   Slonim, Donna ^a  

^a Tufts University   (United States)

Author keywords

Anomaly detection; Feature selection; Machine learning

Indexed keywords

ANOMALY DETECTION; BELONG TO; CHOSEN REPRESENTATION; DATA POINTS; DATA SETS; DENSITY-BASED APPROACHES; FEATURE MODELS; FEATURE SELECTION; FEATURE SPACE; MACHINE LEARNING; NEW APPROACHES; OVER CURRENT; SEMI-SUPERVISED; TRAINING DATA; TRAINING EXAMPLE; TRAINING SETS;

DATA MINING; INFORMATION THEORY; LEARNING SYSTEMS;

FEATURE EXTRACTION;

EID: 79951731505     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.140     Document Type: Conference Paper

References (20)

1. D.J. Newman A. Asuncion. UCI machine learning repository, 2007. http://www.ics.uci.edu/~mlearn/MLRepository.html.
2. C. C. Aggarwal and P. S. Yu. Outlier detection for high dimensional data. In Proceedings of the ACM SIGMOD International Conference on Data Management, pages 37-46, 2001.
3. M.M. Breunig, H.P. Kriegel, R.T. Ng, and J. Sander. LOF: identifying density-based local outliers. ACM SIGMOD Record, 29(2):93-104, 2000.
4. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41(3):1-58, 2009.
5. C-C Chang and C-J Lin. LIBSVM: A library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. daemon9/route/infinity. Project neptune. Phrack, 7(48), 1996.
7. J. P. Early and C. E. Brodley. Behavioral features for network anomaly detection. Machine Learning and Data Mining for Computer Security, pages 107-124, 2005.
8. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874, 2008.
9. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 2009.
10. S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori. Inlier-based outlier detection via direct density ratio estimation. In Eighth IEEE International Conference on Data Mining, 2008. ICDM'08, pages 223-232, 2008.
11. Y.-A. Huang, W. Fan, W. Lee, and P. S. Yu. Cross-feature analysis for detecting ad-hoc routing anomalies. In ICDCS '03: Proceedings of the 23rd International Conference on Distributed Computing Systems, Washington, DC, USA, 2003. IEEE Computer Society.
12. G. John and P. Langley. Estimating continuous distributions in Bayesian classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 338-345. Morgan Kaufmann, 1995.
13. A. Lazarevic and V. Kumar. Feature bagging for outlier detection. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 157-166, 2005. (Pubitemid 43218277)
14. F.T. Liu, K.M. Ting, and Z.H. Zhou. Isolation forest. In Eighth IEEE International Conference on Data Mining, 2008. ICDM'08, pages 413-422, 2008.
15. M. Molla. Novel Uses for Machine Learning and Other Computational Methods for the Design and Interpretation of Genetic Microarrays. PhD thesis, Department of Computer Sciences, University of Wisconsin, Madison, WI, 2007.
16. B. Schölkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12(5):1207-1245, 2000.
17. C. E. Shannon and W. Weaver. A mathematical theory of communication. Bell Syst. Tech. J., 27:379-423 (PartI), 1948.
18. K. A. Spackman. Signal detection theory: Valuable tools for evaluating inductive learning. In Proceedings of the sixth international workshop on Machine learning, pages 160-163. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 1989.
19. J. Tang, Z. Chen, A.W. Fu, and D.W. Cheung. Enhancing effectiveness of outlier detections for low density patterns. Lecture notes in computer science, pages 535-548, 2002.
20. C-M Teng. Correcting noisy data. In ICML, pages 239-248, San Francisco, CA, 1999. Morgan Kaufmann.