  Network security engineers work to keep services available all the time by
handling intruder attacks. Intrusion Detection System (IDS) is one of the
obtainable mechanism that used to sense and classify any abnormal actions.
Therefore, the IDS must be always up to date with the latest intruder attacks
signatures to preserve confidentiality, integrity and availability of the
services. The speed of the IDS is very important issue as well learning the new
attacks. This research work illustrates how the Knowledge Discovery and Data
Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for
testing and evaluating different Machine Learning Techniques. It mainly focuses
on the KDD preprocess part in order to prepare a decent and fair experimental
data set. The techniques J48, Random Forest, Random Tree, MLP, Na\"ive Bayes
and Bayes Network classifiers have been chosen for this study. It has been
proven that the Random forest classifier has achieved the highest accuracy rate
for detecting and classifying all KDD dataset attacks, which are of type (DOS,
R2L, U2R, and PROBE)
