Detecting Intrusion in Data Mining using Naive Bayes Algorithm

With the tremendous increase of network-based services and information sharing on networks, network security is getting more and more importance than ever. Intrusion poses a serious security risk in a network environment. The human classification of the available network audit data instances is usually tedious, time consuming and expensive. Data mining has become a very useful technique for detecting network intrusions by extracting useful knowledge from large number of network data or logs. Naive Bayes classifier is one of the most popular data mining algorithms for classification, which provides an optimal way to predict the class of an unknown example. 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.