Performance Measuring Tool for Data Mining Techniques in Intrusion Detection System

The research project is about to develop a performance measurement tool for Data
Mining (OM) techniques in Intrusion Detection System (IDS). Basically, IDS is a
network security system that is used to detect cyber attacks intrusion. By applying the
Data Mining technique it might improve its accuracy as well as its efficiency in
intrusion detection process especially in a large and fast network. However, there are
various kinds of techniques in OM that can be used to enhance the intrusion detection
process in IDS such as K-mean clustering, Support Vector Machine (SVM), Self
Organizing Maps (SOM), Neural Networks, etc. Therefore, a performance
measurement is required in order determine the best OM technique to be used
depending on the network environment and the type of the IDS used. The
performance measurement takes place at the final stage of the Knowledge Data
Discovery (KDD) process which a step by step procedure in implementing the DM
techniques. With the help of this new tool, it can reduce the human intervention in
performance measurement process as much as possible by replacing the manual tasks
with the automated approach. As a result, errors due to human conducts can be
reduced. This is because a slight of error might affect the overall performance results
measured. The final results are so important that it is to be used in decision making of
the implementation of OM technique in IDS. The tool is comprised of three main
modules: confusion matrix analysis, calculation of the detection rates and the false
alarm rates, and generating the ROC curves as the final result.