  Network security engineers work to keep services available all the time by
handling intruder attacks. Intrusion Detection System (IDS) is one of the
obtainable mechanisms that is 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 a 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 J48, MLP, and Bayes Network classifiers have been chosen for this
study. It has been proven that the J48 classifier has achieved the highest
accuracy rate for detecting and classifying all KDD dataset attacks, which are
of type DOS, R2L, U2R, and PROBE.
