Comparative Study of Clustering for Intrusion Detection in Machine Learning

Abstract

For the current era, it is very much necessary to find the proper data mining techniques for the accuracy of the result. Network security plays a very important role in the intrusion detection. The clustering algorithms applied to intrusion detection techniques which can perform the clustering for NSL dataset. In the given method, we compared big dataset for the various clustering methods. The data mining algorithms applied for NSL-KDD dataset and outputs are recorded for the accuracy percentage and time taken to complete the clustering process. It has been observed that fathers first clustering algorithm has given the output in very less time complexity.