CESVM: Centered hyperellipsoidal support vector machine based anomaly detection

IEEE International Conference on Communications

Volumn , Issue , 2008, Pages 1610-1614

  Rajasegarar, Sutharshan ^a   Leckie, Christopher ^a   Palaniswami, Marimuthu ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; COMPUTATIONAL COMPLEXITY; COMPUTER NETWORKS; ELECTRIC FAULT CURRENTS; FAULT DETECTION; GEARS; IMAGE RETRIEVAL; INTERNET; INTRUSION DETECTION; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS;

ANOMALY DETECTIONS; INTERNATIONAL CONFERENCES; ONE-CLASS SUPPORT VECTOR MACHINES;

VECTORS;

EID: 51249089432     PISSN: 05361486     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICC.2008.311     Document Type: Conference Paper

References (16)

1. V. Barnett and T. Lewis, Outliers in Statistical Data. John Wiley and Sons, 3rd ed., 1994.
2. V. N. Vapnik, Statistical Learning Theory. John Wiley & Sons, 1998.
3. B. Scholkopf and A. Smola, Learning with Kernels. MIT Press, 2002.
4. B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation., vol. 13, no. 7, pp. 1443-1471, 2001.
5. C. Campbell and K. P. Bennett, "A linear programming approach to novelty detection," in Adv. in Neural Inf. Process. Sys., vol. 14, 2001.
6. D. M. J. Tax and R. P. W. Duin, "Support vector data description," Machine Learning, vol. 54, no. 1, pp. 45-66, 2004.
7. D. Wang, D. S. Yeung, and E. C. C. Tsang, "Structured one-class classification," IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 36, no. 6, pp. 1283-1295, 2006.
8. J. H. Ward, "Hierarchical grouping to optimize an objective function," Journal of the American Statistical Association, vol. 58, no. 301, pp. 236-244, 1963.
9. P. Laskov, C. Schafer, and I. Kotenko, "Intrusion detection in unlabeled data with quarter sphere support vector machines," in Detection of Intrusions and Malware & Vulnerability Assessment, (Dortmund), 2004.
10. B. Scholkopf, A. J. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998.
11. G. H. Golub and C. F. V. Loan, Matrix Computations. Maryland: The John Hopkins Uni. Press, 3 ed., 1996.
12. S. G. Nash and A. Sofer, Linear and nonlinear programming. McGraw-Hill, 1996.
13. M. Epelman and R. M. Freund, "A new condition measure, preconditioners, and relations between different measures of conditioning for conic linear systems," SIAM J. on Opt., vol. 12, no. 3, pp. 627-655, 2002.
14. S. Rajasegarar, C. Leckie, M. Palaniswami, and J. C. Bezdek, "Quarter sphere based distributed anomaly detection in wireless sensor networks," in IEEE International Conference on Communications, (Glasgow), pp. 3864-3869, June 2007.
15. R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, and D. Estrin, "Habitat monitoring with sensor networks," CACM, vol. 47, no. 6, pp. 34-40, 2004.
16. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.