A Review of Intrusion Detection System Using Fuzzy K-Means and Naive Bayes Classification

Intrusion Detection Systems (IDSs) are proposed to improve computer security because it is not feasible to build completely secure systems. In particular, IDSs are used to identify, assess, and report unauthorized or unapproved network activities so that appropriate actions may be taken to prevent any future damage. Intrusion Detection System is classified on the basis of the source of Data and Model of Intrusion. There are some challenges faced by the Intrusion Detection System. Fuzzy K-Mean and Naive Bayes classification are the approaches through which the challenges can be overwhelmed. Anomaly in the Anomaly based Intrusion Detection System can be detected using various Anomaly detection techniques. Dimension Reduction can be done using Principle Component Analysis. Support Vector Machine can be used to specify the classifier construction problem. The paper describes the various approaches of Intrusion detection system in briefly.