UNPCC: A novel unsupervised classification scheme for network intrusion detection

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2006, Pages 743-750

  Xie, Zongxing ^a   Quirino, Thiago ^a   Shyu, Mei Ling ^a   Chen, Shu Ching ^b   Chang, LiWu ^c  

^a University of Miami College of Engineering   (United States)

^b FLORIDA INTERNATIONAL UNIVERSITY   (United States)

^c NAVAL RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; NETWORK SECURITY; TELECOMMUNICATION TRAFFIC; UNSUPERVISED LEARNING;

DETECTION RATES; UNSUPERVISED CLASSIFIERS;

INTRUSION DETECTION;

EID: 38949141928     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2006.115     Document Type: Conference Paper

References (16)

1. D. Barbara, N. Wu, and S. Jajodia. Detecting novel network intrusions using Bayes estimator. In First SIAM Conference on Data Mining, Chicago, IL, USA, April 2001.
2. A. Ghosh, A. Schwartzbard, and M. Schatz. Learning program behavior profiles for intrusion detection. In The 1st USENIX Workshop on Intrusion Detection and Network Monitoring, pages 51-62, Santa Clara, USA, 1999.
3. KDD. KDD Cup 1999 Data. http://kdd.ics.uci.edu/databases/kddcup99/ kddcup99.html, 1999.
4. R. Lark. A reappraisal of unsupervised classification. I Correspondence between spectral and conceptual classes. International Journal of Remote Sensing, 16:1425-1443, 1995.
5. R. Lark. A reappraisal of unsupervised classification. II Optimal adjustment of the map legend and a neighbourhood approach for mapping legend units. International Journal of Remote Sensing, 16:1445-1460, 1995.
6. P. Laskov, P. Dussel, C. Schafer, and K. Rieck. Learning intrusion detection: supervised or unsupervised? In ICIAP, Cagliari, ITALY, October 2005.
7. W. Leea, S. Stolfo, and K. Mok. A data mining framework for building intrusion detection models. In IEEE Symposium on Security and Privacy, pages 120-132, Santa Clara, USA, 1999.
8. MNSU. The EM algorithm for unsupervised clustering. http://grb.mnsu. edu/grbts/doc/manual/Expectation-Maximization-EM.html.
9. S. Mukkamala, G. Janoski, and A. Sung. Intrusion detection using neural networks and support vector machines. IEEE Internation Joint Conference on Neural Networks, pages 1702-1707, 2002.
10. M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, and L. Chang. A novel anomaly detection scheme based on principal component classifier. In IEEE Foundations and New Directions of Data Mining Workshop, in conjunction with ICDM'03, pages 171-179, Melbourne, Florida, USA, November 2003.
11. M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, and L. Chang. Principal component-based anomaly detection scheme. In Foundations and Novel Approaches in Data Mining, volume 9, pages 311-329. T.Y. Lin, S. Ohsuga, C.J. Liau, and X. Hu, editors, Springer-Verlag, 2006.
12. L. I. Smith. A tutorial on principal components analysis, http://www.snl.salk.edu/shlens/pub/notes/pca.pdf, February 2002.
13. Weka. http://www.cs.waikato.ac.nz/ml/weka/.
14. Weka. Class DensityBasedClusterer. http://www.dbs.informatik. unimuenchen.de/~zimek/diplomathesis/implementations/EHNDs/doc/weka/clusterers/ DensityBasedClusterer. html.
15. Wikipedia. Expectation-Maximization algorithm. http://en.wikipedia. org/wiki/Expectation-maximization-algorithm.
16. Z. Xie, T. Quirino, M.-L. Shyu, S.-C. Chen, and L. Chang. A distributed agent-based approach to intrusion detection using the lightweight pec anomaly detection classier. In IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC2006), pages 446-453, Taichung, Taiwan, R.O.C., June 2006.