  As an indispensable defensive measure of network security, the intrusion
detection is a process of monitoring the events occurring in a computer system
or network and analyzing them for signs of possible incidents. It is a
classifier to judge the event is normal or malicious. The information used for
intrusion detection contains some redundant features which would increase the
difficulty of training the classifier for intrusion detection and increase the
time of making predictions. To simplify the training process and improve the
efficiency of the classifier, it is necessary to remove these dispensable
features. in this paper, we propose a novel LA-SVM scheme to automatically
remove redundant features focusing on intrusion detection. This is the first
application of learning automata for solving dimension reduction problems. The
simulation results indicate that the LA-SVM scheme achieves a higher accuracy
and is more efficient in making predictions compared with traditional SVM.
