An efficient intrusion detection system using a boosting-based learning algorithm

International Journal of Computer Applications in Technology

Volumn 27, Issue 4, 2006, Pages 223-231

  Zhenwei, Yu ^a   Tsai, Jeffrey J P ^a  

^a 1120 Science and Engineering Offices   (United States)

Author keywords

Boosting algorithm; BP neural network; Intrusion detection; KDDCUP'99 contest; Misclassification cost; Prediction confidence

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; LEARNING SYSTEMS; NEURAL NETWORKS;

DATASETS; INTRUSION DETECTION; INTRUSION DETECTION SYSTEM; KDDCUP'99 CONTEST; MISCLASSIFICATION COST; PREDICTION CONFIDENCE;

LEARNING ALGORITHMS;

EID: 33847094245     PISSN: 09528091     EISSN: None     Source Type: Journal    
DOI: 10.1504/IJCAT.2006.011994     Document Type: Article

References (16)

1. Agarwal, R. and Joshi, V.M. (2001) 'PNrule: a new framework for learning classifier models in data mining (a case-study in network intrusion detection)', Proceedings of the 1st SIAM Conference on Data Mining, April, Chicago, IL.
2. Cohen, W. (1995) 'Fast effective rule induction', Proceedings of the 12th International Conference on Machine Learning. pp. 115-123.
3. Cohen, W. and Singer, Y. (1999) 'A simple, fast, and effective rule learner', Proceedings of Annual Conference of American Association for Artificial Intelligence, pp.335-342.
4. Elkan, C. (2000) 'Results of the KDD'99 classifier learning', ACM SIGKDD Explorations, Vol. 1, No. 2, pp.63, 64.
5. Ertoz, L., Steinbach, M. and Kumar, V. (2003) 'Finding clusters of different sizes, shapes and densities in noisy high dimensional data'. Proceedings of the 3rd SIAM International Conference on Data Mining, May, San Francisco, CA.
6. Giacinto, G., Roli, F. and Didaci, L. (2003) 'A modular multiple classifier system for the detection of intrusions in computer networks', Proceedings of the 4th International Workshop on Multiple Classifier Systems (MCS2003), pp.346-355.
7. Gomez, J. and Dasgupta, D. (2002) 'Evolving fuzzy classifiers for intrusion detection', CD-ROM Proceedings of the 3rd Annual Information Assurance Workshop, IEEE Computer Society Press and the National Security Agency, United States Military Academy, West Point, NY.
8. Kumar, V. (2002) Data Mining for Network Intrusion Detection; Experience with KDDCup'99 Data set, Presentation in Workshop on Network Intrusion Detection, March, Harford Community College, Aberdeen, Maryland.
9. Lee, W., Stolfo, S. and Chan, P. (2001) 'Real time data mining-based intrusion detection', Proceedings of the 2nd DARPA Information Survivability Conference & Exposition (DISCEX II'01), Vol. I, pp.0089-0100.
10. Lee, W., Stolfo, S. and Mok, K. (1999) 'A data mining framework for building intrusion detection models', Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120-130.
11. Levin, I. (2000) KDD-99 Classifier Learning Contest LLSOFT'S Results Overview, ACM SIGKDD Explorations, Vol. 1, No. 2, pp.67-75.
12. Pfahringer, B. (2000) 'Winning the KDD99 classification cup: bagged boosting', ACM SIGKDD Explorations, Vol. 1, No. 2, pp.65, 66.
13. Robert, S. and Yoram, S. (1999) 'Improved boosting algorithms using confidence-rated predictions', Machine Learning, Vol. 37, No. 3, pp.297-336.
14. Sabhnani, M. (2002) Development of an Intrusion Detection System through Machine Learning and Rule Based Algorithms for Networked Computing, Thesis Presentation in EECS Dept. Univ. of Toledo.
15. Sabhnani, M. and Serpen, G. (2003) 'Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context', Proceedings of International Conference on Machine Learning: Models, Technologies and Applications, pp.209-215.
16. Yeung, D-Y. and Chow, C. (2002) 'Parzen-window network intrusion detectors', Proceedings of 6th International Conference on Pattern Recognition, Vol. 4, pp.385-388.