  Intrusion detection systems (IDS) are widely studied by researchers nowadays
due to the dramatic growth in network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes intrusion detection
systems of great importance. Existing approaches to improve intrusion detection
systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the accuracy as well as the
learning time. In this paper we propose a hybrid feature selection method using
Correlation-based Feature Selection and Information Gain. In our work we apply
adaptive boosting using na\"ive Bayes as the weak (base) classifier. The key
point in our research is that we are able to improve the detection accuracy
with a reduced number of features while precisely determining the attack.
Experimental results showed that our proposed method achieved high accuracy
compared to methods using only 5-class problem. Correlation is done using
Greedy search strategy and na\"ive Bayes as the classifier on the reduced
NSL-KDD dataset.
