  Network intrusion detection systems are an active area of research to
identify threats that face computer networks. Network packets comprise of high
dimensions which require huge effort to be examined effectively. As these
dimensions contain some irrelevant features, they cause a high False Alarm Rate
(FAR). In this paper, we propose a hybrid method as a feature selection, based
on the central points of attribute values and an Association Rule Mining
algorithm to decrease the FAR. This algorithm is designed to be implemented in
a short processing time, due to its dependency on the central points of feature
values with partitioning data records into equal parts. This algorithm is
applied on the UNSW-NB15 and the NSLKDD data sets to adopt the highest ranked
features. Some existing techniques are used to measure the accuracy and FAR.
The experimental results show the proposed model is able to improve the
accuracy and decrease the FAR. Furthermore, its processing time is extremely
short.
