The Application on Intrusion Detection based on K-means Cluster Algorithm
International Journal for Research in Applied Science and Engineering Technology
In today's scenario networking is the most essential part of the communication. Individuals can do a lot of things on the internet. Its security has been one of the most important problems in the world. Network attacks have increased over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to protect the network. In recent years, many researchers are using data mining techniques for building IDS. A wide variety of data mining techniques have been
... hniques have been applied to intrusion detections. In data mining, clustering is the most important unsupervised learning process used to find the structures or patterns in a collection of un-labelled data. In this paper, we present an Intrusion Detection method using K-means clustering to cluster and analyse the data, Neuro-fuzzy models, Support vector machine (SVM) and C4.5 algorithm. Computer simulations show that this method can detect unknown intrusions efficiently in the real network connections.