Anomaly detection via online oversampling principal component analysis

IEEE Transactions on Knowledge and Data Engineering

Volumn 25, Issue 7, 2013, Pages 1460-1470

  Lee, Yuh Jye ^a   Yeh, Yi Ren ^b   Wang, Yu Chiang Frank ^c  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c RESEARCH CENTER FOR INFORMATION TECHNOLOGY INNOVATION   (Taiwan)

Author keywords

Anomaly detection; least squares; online updating; oversampling; principal component analysis

Indexed keywords

ANOMALY DETECTION; ANOMALY DETECTION METHODS; ANOMALY-DETECTION ALGORITHMS; CREDIT CARD FRAUD DETECTIONS; LEAST SQUARE; ONLINE UPDATING; OVER SAMPLING; PRINCIPAL DIRECTIONS;

COVARIANCE MATRIX; DATA MINING; INTRUSION DETECTION;

PRINCIPAL COMPONENT ANALYSIS;

EID: 84878266577     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2012.99     Document Type: Article

References (26)

1. D.M. Hawkins, Identification of Outliers. Chapman and Hall, 1980.
2. M. Breunig, H.-P. Kriegel, R.T. Ng, and J. Sander, "LOF: Identifying Density-Based Local Outliers," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2000.
3. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, pp. 15:1-15:58, 2009.
4. L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A.D. Joseph, and N. Taft, "In-Network Pca and Anomaly Detection," Proc. Advances in Neural Information Processing Systems 19, 2007.
5. H.-P. Kriegel, M. Schubert, and A. Zimek, "Angle-Based Outlier Detection in High-Dimensional Data," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and data Mining, 2008.
6. A. Lazarevic, L. Ertöz, V. Kumar, A. Ozgur, and J. Srivastava, "A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection," Proc. Third SIAM Int'l Conf. Data Mining, 2003.
7. X. Song, M. Wu, and C.J., and S. Ranka, "Conditional Anomaly Detection," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 5, pp. 631-645, May 2007.
8. S. Rawat, A.K. Pujari, and V.P. Gulati, "On the Use of Singular Value Decomposition for a Fast Intrusion Detection System," Electronic Notes in Theoretical Computer Science, vol. 142, no. 3, pp. 215-228, 2006. (Pubitemid 41828101)
9. W. Wang, X. Guan, and X. Zhang, "A Novel Intrusion Detection Method Based on Principal Component Analysis in Computer Security," Proc. Int'l Symp. Neural Networks, 2004.
10. F. Angiulli, S. Basta, and C. Pizzuti, "Distance-Based Detection and Prediction of Outliers," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 2, pp. 145-160, 2006. (Pubitemid 43281190)
11. V. Barnett and T. Lewis, Outliers in Statistical Data. John Wiley&Sons, 1994.
12. W. Jin, A.K.H. Tung, J. Han, and W. Wang, "Ranking Outliers Using Symmetric Neighborhood Relationship," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, 2006.
13. N.L.D. Khoa and S. Chawla, "Robust Outlier Detection Using Commute Time and Eigenspace Embedding," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, 2010.
14. E.M. Knox and R.T. Ng, "Algorithms for Mining Distance-Based Outliers in Large Data Sets," Proc. Int'l Conf. Very Large Data Bases, 1998.
15. H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek, "Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data," Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining, 2009.
16. C.C. Aggarwal and P.S. Yu, "Outlier Detection for High Dimensional Data," Proc. ACM SIGMOD Int'l Conf. Management of Data, 2001.
17. D. Pokrajac, A. Lazarevic, and L. Latecki, "Incremental Local Outlier Detection for Data Streams," Proc. IEEE Symp. Computational Intelligence and Data Mining, 2007.
18. T. Ahmed, "Online Anomaly Detection using KDE," Proc. IEEE Conf. Global Telecomm., 2009.
19. Y.-R. Yeh, Z.-Y. Lee, and Y.-J. Lee, "Anomaly Detection via Oversampling Principal Component Analysis," Proc. First KES Int'l Symp. Intelligent Decision Technologies, pp. 449-458, 2009
20. G.H. Golub and C.F.V. Loan, Matrix Computations. Johns Hopkins Univ. Press, 1983.
21. R. Sibson, "Studies in the Robustness of Multidimensional Scaling: Perturbational Analysis of Classical Scaling," J. Royal Statistical Soc. B, vol. 41, pp. 217-229, 1979.
22. B. Yang, "Projection Approximation Subspace Tracking," IEEE Trans. Signal Processing, vol. 43, no. 1, pp. 95-107, Jan. 1995.
23. S. Papadimitriou, J. Sun, and C. Faloutsos, "Streaming Pattern Discovery in Multiple Time-Series," Proc. 31st Int'l Conf. Very Large Data Bases, 2005.
24. S. Haykin, Adaptive Filter Theory. Prentice Hall, 1991.
25. A. Asuncion and D. Newman, "UCI Repository of Machine Learning Databases," http://www.ics.uci.edu/mlearn/mlrepository.html, 2007.
26. A.P. Bradley, "The Use of the Area under the Roc Curve in the Evaluation of Machine Learning Algorithms," Pattern Recognition, vol. 30, pp. 1145-1159, 1997. (Pubitemid 127406521)