With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify the network events as either normal events or attack events. This research study claims that the Hidden Naive Bayes (HNB) model can be applied to intrusion detection problems that suffer from dimensionality, highly correlated features, and high network data stream volumes. HNB is a data mining model that relaxes the naive Bayes method's conditional independence assumption. The experimental results show that the HNB model exhibits a superior overall performance in terms of accuracy, error rate, and misclassification cost compared with the traditional naive Bayes model, leading extended naive Bayes models and the Knowledge Discovery and Data Mining (KDD) Cup 1999 winner. HNB model performed better than other leading state-of-the art models, such as Support Vector Machine, in predictive accuracy. The results also indicate that HNB model significantly improves the accuracy of detecting denial-of-services (DoS) attacks. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]