Abstract: A computer network is a collection of computer systems and other hardware devices that are linked together through communication channels to facilitate communication and resource-sharing among a wide range of users. Even though static defence mechanisms such as firewalls and software updates can provide a reasonable level of security, more dynamic mechanisms such as Intrusion Detection Systems (IDSs) should also be utilized. Different detection techniques can be employed to search for attack patterns in the data monitored. Misuse detection and anomaly detection are the most widely used techniques. But they have their own drawbacks. To overcome those issues, hybrid methods are used. Hybrid classifiers are able to provide improved detection accuracy, but usually have a complex structure and high computational costs. Hence a new intrusion detection model is introduced in which feature selection is carried out and then with the selected features classification is performed. The binary bat algorithm is used for feature selection in Hadoop environment and then the samples with selected features are trained and tested using na´ve bayes classifier. Since it is carried out in a distributed environment, the execution time is greatly reduced and the detection rate is improved.

Keywords: Intrusion Detection System, Hybrid classifier, Naive bayes, Binary bat optimization.