@article{(Open Science Index):https://publications.waset.org/pdf/6516,
	  title     = {Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification},
	  author    = {Dewan Md. Farid and  Jerome Darmont and  Nouria Harbi and  Nguyen Huu Hoa and  Mohammad Zahidur Rahman},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {2762 - 2766},
	  ee        = {https://publications.waset.org/pdf/6516},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},