Extreme learning machines for intrusion detection

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Cheng, Chi ^a   Tay, Wee Peng ^a   Huang, Guang Bin ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Extreme Learning Machines; intrusion detection; random features; Support Vector Machines

Indexed keywords

DETECTION ACCURACY; EXTREME LEARNING MACHINE; MACHINE LEARNING METHODS; MULTI-CLASS CLASSIFICATION; PARAMETER-TUNING; RANDOM FEATURES; TRAINING AND TESTING;

CLASSIFIERS; INTRUSION DETECTION; NEURAL NETWORKS;

SUPPORT VECTOR MACHINES;

EID: 84865067272     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252449     Document Type: Conference Paper

References (25)

1. S. Bosworth and M. E. Kabay, Computer Security Handbook, 4th ed. Wiley, 2002.
2. S. Mukkamala, G. Janoski, and A. Sung, "Intrusion detection using neural networks and support vector machines," in Proceedings of the International Joint Conference on Neural Networks, vol. 2, May 2002, pp. 1702-1707.
3. G.-B. Huang, D. H. Wang, and Y. Lan, "Extreme learning machines: a survey," International Journal of Machine Leaning and Cybernetics, vol. 2, no. 2, pp. 107-122, 2011.
4. G.-B. Huang, H. M. Zhou, X. J. Ding, and R. Zhang, "Extreme learning machines for regression and multiclass classification," IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 42, no. 2, pp. 513-529, 2011.
5. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," in Proceedings of IEEE International Joint Conference on Neural Networks, vol. 2, 2004, pp. 985-990.
6. (1999) KDDCUP dataset. [Online]. Available: http://kdd.ics.uci.edu/ databases/kddcup99/kddcup99.html
7. S. Mukkamala and A. Sung, "Detecting denial of service attacks using support vector machines," in Proceedings of the 12th IEEE International Conference on Fuzzy Systems, 2003.
8. M. Luo, L. Wang, H. Zhang, and J. Chen, "A research on intrusion detection based on unsupervised clustering and support vector machine," in Information and Communications Security, ser. Lecture Notes in Computer Science, S. Qing, D. Gollmann, and J. Zhou, Eds. Springer Berlin / Heidelberg, 2003, vol. 2836, pp. 325-336.
9. D. Kim and J. Park, "Network-based intrusion detection with support vector machines," in Information Networking, ser. Lecture Notes in Computer Science, H.-K. Kahng, Ed. Springer Berlin / Heidelberg, 2003, vol. 2662, pp. 747-756.
10. G. Tandon, "Weighting versus pruning in rule validation for detecting network and host anomalies," in In Proceedings of the 13th ACM SIGKDD international. ACM Press, 2007.
11. Y. Liao and V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection," Computers & Security, vol. 25, pp. 439-448, 2002.
12. L. Portnoy, E. Eskin, and S. Stolfo, "Intrusion detection with unlabeled data using clustering," in In Proceedings of ACM CSS Workshop on Data Mining Applied to Security, 2001, pp. 5-8.
13. G. Thatte, U. Mitra, and J. Heidemann, "Parametric methods for anomaly detection in aggregate traffic," IEEE/ACM Transactions on Networking, vol. 19, no. 2, pp. 512-525, April 2011.
14. M. Qin and K. Hwang, "Frequent episode rules for internet anomaly detection," in Proceedings of the Network Computing and Applications, Third IEEE International Symposium. Washington, DC, USA: IEEE Computer Society, 2004, pp. 161-168.
15. X. He, C. Papadopoulos, J. Heidemann, U. Mitra, and U. Riaz, "Remote detection of bottleneck links using spectral and statistical methods," Computer Networks, vol. 53, pp. 279-298, February 2009.
16. W. W. Streilein, R. K. Cunningham, and S. E. Webster, "Improved detection of low-profile probe and denial-of-service attacks," in Proceedings of the 2001 Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, June 2001.
17. C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
18. C.-W. Hsu and C.-J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
19. J. H. Friedman, "Another approach to polychotomous classification," Department of Statistics, Stanford University, Tech. Rep., 1996.
20. C. R. Rao and S. K. Mitra, Generalized Inverse of Matrices and Its Applications. John Wiley & Sons Inc, 1972.
21. S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan, "Costbased modeling for fraud and intrusion detection: results from the JAM project," in Proceedings of DARPA Information Survivability Conference and Exposition, vol. 2, January 2002, pp. 130-144.
22. C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification," Bioinformatics, vol. 1, no. 1, pp. 1-16, 2010.
23. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 27:1-27:27, 2011, software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.
24. (2004) ELM MATLAB code. [Online]. Available: http://www.ntu.edu.sg/home/ egbhuang/ELM Codes.htm
25. S. Canu, Y. Grandvalet, V. Guigue, and A. Rakotomamonjy, "SVM and kernel methods MATLAB toolbox," Perception Systmes et Information, INSA de Rouen, Rouen, France, 2005. [Online]. Available: http://asi.insa-rouen.fr/ enseignants/arakotom/toolbox/index.html