Contents I. Introduction
Intrusion detection refers to a broad range of approaches that detect malicious attacks on computers and networks. Generally speaking, these approaches can be categorized into misuse detection and anomaly detection.
Abstract:
Kernel methods are widely used in statistical learning for many fields, such as protein classification and image processing. We recently extend kernel methods to intrusio...Show MoreMetadata
Abstract:
Kernel methods are widely used in statistical learning for many fields, such as protein classification and image processing. We recently extend kernel methods to intrusion detection domain by introducing a new family of kernels suitable for intrusion detection. These kernels, combined with an unsupervised learning method - one-class support vector machine, are used for anomaly detection. Our experiments show that the new anomaly detection methods are able to achieve better accuracy rates than the conventional anomaly detectors.
Date of Conference: 10-11 June 2004
Date Added to IEEE Xplore: 06 June 2005
Print ISBN:0-7803-8572-1
References is not available for this document.
Select All
1.
Christopher Kruegel and Thomas Toth. Distributed pattern detection for intrusion detection. In Network and Distributed System Security Symposium Conference Proceedings: 2002, 1775 Wiehle Ave., Suite 102, Reston, Virginia 20190, U.S.A., 2002. Internet Society.
2.
T. Lunt. A real-time intrusion detection expert system (IDES). In Final Report SRI-CSL-92-05, 1992.
3.
T. Lunt and R. Jagannathan. A prototype real-time intrusion detection system. In IEEE Symposium on Security and Privacy, 1988.
4.
S. Forrest, S. A. Hofineyer, and A. Somayaji. Computer immunology. Comm. ACM, 40(10):88-96, Oct. 1997.
5.
Roy A. Maxion and Kymie M. C. Tan. Anomaly detection in embedded systems. IEEE Trans. Computers, 51(2):108-120, Feb. 2002.
6.
Thomas M. Mitchell. Machine Learning. McGraw-Hill Higher Education, 1997.
7.
William W. Cohen. Fast effective rule induction. In Proceedings of the Twelfth International Conference on Machine Learning, 1995.
8.
W. Lee and S. Stolfo. Data mining approaches for intrusion detection. In Proceedings of the 7th USENIX Security Symposium, San Antonio, TX, 1998.
9.
Wenke Lee and Sal Stolfo. A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security, 3(4), Nov 2000.
10.
W. Lee, S. Stolfo, and K. Mok. A Data Mining Framework for Building Intrusion Detection Models. In IEEE Symposium on Security and Privacy, pages 120-132, 1999.
11.
James Cannady. Artificial neural networks for misuse detection. http://citeseer.nj.nec.com/cannady98artificial.html, 1998.
12.
H. Debar, M. Becke, and D. Siboni. A neural network component for an intrusion detection system. In Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.
13.
Jake Ryan, Meng-Jang Lin, and Risto Miikkulainen. Intrusion detection with neural networks. In Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998.
14.
Kymie Tan. The application of neural networks to UNIX computer security, http://citeseer.nj.nec.com/tan95application.html, 1995.
15.
V. Honavar L. Miller G. Helmer, J. S. K. Wong. Feature selection using a genetic algorithm for intrusion detection. In Proceedings on Genetic and Evolutionary Computation Conference, Orlando, FL., 1999.
16.
W. Lee and S. Stolfo. Data mining approaches for intrusion detection. In 7th USENIX Security Symposium, San Antonio, TX, 1998.
17.
S. Mukkamaka and A. H. Sung. Learning machines for intrusion detection: Support vector machines and neural networks. In Proceedings of the International Conference on Security and Management, pages 525-531, 2002.
18.
S. Mukkamala and A. H. Sung. Feature selection for intrusion detection using neural networks and support vector machines. Journal of the Transportation Research Board (of the National Academies). To appear.
19.
S. Mukkamala A. H. Sung. Identifying important features for intrusion detection using support vector machines and neural networks. In Symposium on Applications and the Internet, 2003.
20.
T. M. Mitchell. Machine Learning. McGraw-Hill Higher Education, 1997.
21.
X. Li and N. Ye. Decision tree classifiers for computer intrusion detection. Journal of Parallel and Distributed Computing Practices, 4(2), 2003.
22.
W. W. Cohen. Fast effective rule induction. In Proc. of the 12th International Conference on Machine Learning, pages 115-123, Tahoe City, CA, 1995.
23.
K. Tan. The application of neural networks to unix computer security. Proceeding of International Conference on Neural Networks, 1995.
24.
J. Cannady. Artificial neural networks for misuse detection. In Proceedings of the 1998 National Information Systems, pages 443-456, 1998.
25.
J. Ryan, M. J. Lin, and R. Miikkulainen. Intrusion detection with neural networks. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998.
26.
D. Siboni H. Debar, M. Becke. A neural network component for an intrusion detection system. In In Proceedings of the IEEE Computer Society Symposium on Research in Security and Privacy, 1992.
27.
J.C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167, 1998.
28.
Vladimir N. Vapnik. Statistical Learning Theory. Wiley, Chichester, GB, 1998.
29.
B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In 5th Annual ACM Workshop on COLT, pages 144-152, Pittsburgh, PA, 1992.
30.
J. Platt. Fast training of support vector machines using sequential minimal optimization, pages 185-208. In B. Schlkopf, C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, 1999.
