Y-means: A clustering method for intrusion detection

Canadian Conference on Electrical and Computer Engineering

Volumn 2, Issue , 2003, Pages 1083-1086

  Guan, Yu ^a   Ghorbani, Ali A ^a   Belacel, Nabil ^b  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Clustering; Intrusion detection; K means; Outlier

Indexed keywords

BANDWIDTH; HEURISTIC METHODS; INFORMATION TECHNOLOGY;

INTRUSION DETECTION;

WORLD WIDE WEB;

EID: 0141540496     PISSN: 08407789     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. KDD Cup 1999 Data. University of California, Irvine, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999.
2. K. Brownlee. Statistical Theory and Methodology. John Wiley and Sons, Inc., New York, 1967.
3. R. O. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley and Sons, Inc., New York, 2001.
4. P. Hansen and N. Mladenovic. J-means: a new local search heuristic for minimum sum-of-squares clustering. Pattern Recognition, 34(2):405-413, 2002.
5. J. MacQueen. Some methods for classification and analysis of multivariate observations. Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, 2:28-297, 1967.
6. L. Portnoy. Intrusion detection with unlabeled data using clustering. Columbia University, 2000.
7. P. Rousseeuw and A. Leroy. Robust Regression and Out-lier Detection. Wiley Series in Probability and Mathematical Statistics. Applied probability and statistics, New York, 1987.