Best hybrid classifiers for intrusion detection

Journal of Computational Methods in Sciences and Engineering

Volumn 6, Issue 5-6, 2006, Pages S299-S307

  Kholfi, Sanaa ^a   Habib, Muhammad ^a   Aljahdali, Sultan ^b  

^a Krasnow Institute for Advanced Study   (United States)

^b UNIVERSITY OF BUSINESS AND TECHNOLOGY   (Saudi Arabia)

Author keywords

And Neural; Intrusion detection; K nearest neighbor K nn; Na?ve Bayes NB; Network security; Optimal hybrid model

Indexed keywords

MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; NETWORK SECURITY;

AND NEURAL; CLASSIFICATION METHODS; COMPUTATIONAL MODEL; HYBRID CLASSIFIER; HYBRID MODEL; INTRUSION DETECTION SOFTWARES; K-NEAREST NEIGHBORS; NAVE BAYES;

INTRUSION DETECTION;

EID: 58149112412     PISSN: 14727978     EISSN: None     Source Type: Journal    
DOI: 10.3233/jcm-2006-6s208     Document Type: Article

References (10)

1. B. Rebecca and M. Peter, Intrusion Detection Systems, NIST Special Publication on IDS, Nov 2001.
2. B.A. Nahla, B. Salem and E. Zied, Naive Bayes vs decision trees in intrusion detection systems, 2004.
3. M. Srinivas, J. Guadalupe and S. Andrew, Intrusion Detection Using Neural Networks and Support Vector Machines.
4. J. Ryan, M.-J. Lin and R. Miikkulainen, Intrusion detection with neural networks, Proceedings of the 1997 conference on Advances in neural information processing systems 10, 943-949, July 1998, Denver, Colorado, United States.
5. Y. Liao arid V. Rao Vemuri, Use of K-nearest neighbor classifier for intrusion detection, Computers and security 21(5) (2002), 439-448.
6. Alduhaiman, Khaled, Computational Machine for Optimal Parsimonious Hybrid Models of E-Text Classification, GMU, 2004.
7. D. Patterson, Artificial Neural Networks. Singapore: Prentice Hall. Good wide-ranging coverage of topics, although less detailed than some other books, 1996.
8. L. Fausett, Fundamentals of Neural Networks, New York: Prentice Hall, 1994.
9. C.J. Whitaker and L.I. Kuncheva, Examining the relationship between majority vote accuracy and diversity in bagging and boosting, Technical Report, 2003, School of Informatics, University of Wales, Bangor.
10. C.J. Van Rijsbergen, Information Retrieval, London, Butterworths, 1979