  Intrusion Detection System (IDS) has increasingly become a crucial issue for
computer and network systems. Optimizing performance of IDS becomes an
important open problem which receives more and more attention from the research
community. In this work, A multi-layer intrusion detection model is designed
and developed to achieve high efficiency and improve the detection and
classification rate accuracy .we effectively apply Machine learning techniques
(C5 decision tree, Multilayer Perceptron neural network and Na\"ive Bayes)
using gain ratio for selecting the best features for each layer as to use
smaller storage space and get higher Intrusion detection performance. Our
experimental results showed that the proposed multi-layer model using C5
decision tree achieves higher classification rate accuracy, using feature
selection by Gain Ratio, and less false alarm rate than MLP and na\"ive Bayes.
Using Gain Ratio enhances the accuracy of U2R and R2L for the three machine
learning techniques (C5, MLP and Na\"ive Bayes) significantly. MLP has high
classification rate when using the whole 41 features in Dos and Probe layers.
