  Intruders detection in computer networks has some deficiencies from machine
learning approach, given by the nature of the application. The principal
problem is the modest display of detection systems based on learning algorithms
under the constraints imposed by real environments. This article focuses on the
machine learning approach for network intrusion detection in batch and data
stream environments. First, we propose and describe three variants of KDD99
dataset preprocessing including attribute selection. Secondly, a thoroughly
experimentation is performed from evaluating and comparing representative batch
learning algorithms on the variants obtained from KDD99 pre processing.
Finally, since network traffic is a constant data stream, which can present
concept drifting with high rate of false positive, along with the fact that
there are not many researches addressing intrusion detection on streaming
environments, lead us to make a comparison of various representative data
stream classification algorithms. This research allows determining the
algorithms that better perform on the proposed variants of KDD99 for both batch
and data stream environments.
