Journal of Communications
Intrusion detection is an important task for network operators in today's Internet. Traditional network intrusion detection systems rely on either specialized signatures of previously seen attacks, or on labeled traffic datasets that are expensive and difficult to re-produce for user-profiling to hunt out network attacks. This paper presents a feature grouping method for the selection of features for intrusion detection. The method is based on mutual information theory and is tested against KDD CUP 99 dataset.