TY - JFULL AU - Dewan Md. Farid and Jerome Darmont and Nouria Harbi and Nguyen Huu Hoa and Mohammad Zahidur Rahman PY - 2009/1/ TI - Adaptive Network Intrusion Detection Learning: Attribute Selection and Classification T2 - International Journal of Computer and Information Engineering SP - 2761 EP - 2766 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/6516 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 36, 2009 N2 - In this paper, a new learning approach for network intrusion detection using naïve Bayesian classifier and ID3 algorithm is presented, which identifies effective attributes from the training dataset, calculates the conditional probabilities for the best attribute values, and then correctly classifies all the examples of training and testing dataset. Most of the current intrusion detection datasets are dynamic, complex and contain large number of attributes. Some of the attributes may be redundant or contribute little for detection making. It has been successfully tested that significant attribute selection is important to design a real world intrusion detection systems (IDS). The purpose of this study is to identify effective attributes from the training dataset to build a classifier for network intrusion detection using data mining algorithms. The experimental results on KDD99 benchmark intrusion detection dataset demonstrate that this new approach achieves high classification rates and reduce false positives using limited computational resources. ER -