Toward building lightweight intrusion detection system through modified RMHC and SVM

ICON 2007 - Proceedings of the 2007 15th IEEE International Conference on Networks

Volumn , Issue , 2007, Pages 83-88

  Chen, You ^a,b   Li, Wen Fa ^a,b   Cheng, Xue Qi ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL RESOURCES; DATA SETS; DETECTION RATES; FEATURE SELECTION; FEATURE SELECTION ALGORITHMS; HILL CLIMBING; INTERNATIONAL CONFERENCES; INTRUSION DETECTION SYSTEM; LIGHTWEIGHT INTRUSION DETECTION SYSTEM; RANDOM MUTATION; SEARCH STRATEGIES; SPEED UPS; WRAPPER APPROACHES; WRAPPER-BASED FEATURE SELECTION;

COMPUTER CRIME; DECISION SUPPORT SYSTEMS; EXPERIMENTS; FEATURE EXTRACTION; INFORMATION MANAGEMENT; INTERNET; PATTERN RECOGNITION; SEARCH ENGINES; SENSORS; SIGNAL DETECTION; SUPPORT VECTOR MACHINES;

INTRUSION DETECTION;

EID: 48149103422     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICON.2007.4444066     Document Type: Conference Paper

References (13)

1. Koller, D., Sahami, M.: "Toward optimal feature selection". In: Proceedings of International Conference on Machine Learning, Bari, Italy, 284-292, 1996
2. You Chen, Yang Li, Xue-Qi Cheng and Li Guo. Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System. Conference on Information Security and Cryptology. LNCS4318, 153-167, 2006
3. J. P. Thomas S. Chebrolu, A. Abraham. Hybrid feature selection for modeling intrusion detection systems. In Proceedings of Lecture Notes in Computer Science, volume 3316, 1020-1025, 2004
4. You Chen, Lei Dai, Yang Li, Xue-Qi Cheng. Building Efficient Intrusion Detection Model Based on Principal Component Analysis and C4.5 Algorithm. 9th IEEE International Conference on Advanced Communication Technology. 2109-2112, 2007
5. Kim, D., Nguyen, H.-N., Ohn, S.-Y., Park, J.: Fusions of GA and SVM for Anomaly Detection in Intrusion Detection System. In.: Wang J., Liao, X., Yi, Z. (eds.): Advances in Neural Networks. Lecture Notes in Computer Science, Vol. 3498. Springer-Verlag, Berlin Heidelberg New York 415-420, 2005.
6. Nigel Williams, Sebastian Zander and Grenville Armitrage, A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification. ACM SIGCOMM Computer Communication Review, Volume 36 5-13, 2006
7. A.K. Jain and D. Zongker, "Feature selection: Evaluation, application, and small sample performance", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no.2, 153-158, 1997.
8. M. Kudo and J. Sklansky, "Comparison of algorithms that select features for pattern classifiers", Pattern Recognition, vol. 33, no. 1, 25-41, 2000.
9. D. B. Skalak, "Prototype and feature selection by sampling and random mutation hill climbing algorithms", Proc. of the Eleventh International Conference on Machine Learning, 293-301, 1994.
10. K. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, Optimization by Simulated Annealing, Science, vol. 220, no. 4598, 1983.
11. Karina Zapien Arreóla, Janis Fehr, Hans Burkhardt: Fast Support Vector Machine Classification using linear SVMs, The 18th International Conference on Pattern Recognition 0-7695-2521-0/06, 2006
12. KDD Cup 1999 Data. available. http://kdd.ics.uci.edu/databases/kddcup99/ kddcup99.html.
13. Chen You. Xue-Qi Cheng, Yang Li, Lei Dai. Lightweight intrusion detection systems based on feature selection. Journal of Software, 18(7):1639-1651, 2007