Machine Learning
Naive Bayes vs decision trees in intrusion detection systems
Proceedings of the 2004 ACM symposium on Applied computing
The Research of Bayesian Classifier Algorithms in Intrusion Detection System
ICEE '10 Proceedings of the 2010 International Conference on E-Business and E-Government
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
ICT-EurAsia'13 Proceedings of the 2013 international conference on Information and Communication Technology
Hi-index | 0.00 |
In this paper, a new learning algorithm for adaptive network intrusion detection using principal component analysis, decision tree and Naïve Bayesian classifier is presented. First we use PCA (Principal Component Analysis) to remove unimportant information like the noise in the data sets, to reduce the dimension, and to retain the important information as much as possible. Then we use the Decision tree and Naive Bayesian algorithm to make Intrusion Detection Model. We have tested the performance of our proposed algorithm on the KDD99 benchmark intrusion detection dataset. The experimental result prove that the proposed algorithm achieved high detection rates (DR), low false positive (FP) and low false negative (FN) for different types of network intrusions.