TY - JFULL AU - Dewan Md. Farid and Nouria Harbi and Suman Ahmmed and Md. Zahidur Rahman and Chowdhury Mofizur Rahman PY - 2010/7/ TI - Mining Network Data for Intrusion Detection through Naïve Bayesian with Clustering T2 - International Journal of Computer and Information Engineering SP - 1052 EP - 1057 VL - 4 SN - 1307-6892 UR - https://publications.waset.org/pdf/13376 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 42, 2010 N2 - Network security attacks are the violation of information security policy that received much attention to the computational intelligence society in the last decades. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naïve Bayesian classifier is one of the most popular data mining algorithm for classification, which provides an optimal way to predict the class of an unknown example. It has been tested that one set of probability derived from data is not good enough to have good classification rate. In this paper, we proposed a new learning algorithm for mining network logs to detect network intrusions through naïve Bayesian classifier, which first clusters the network logs into several groups based on similarity of logs, and then calculates the prior and conditional probabilities for each group of logs. For classifying a new log, the algorithm checks in which cluster the log belongs and then use that cluster-s probability set to classify the new log. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions. ER -