A Survey: Network Intrusion Detection System based on Data Mining Techniques

In spite of growing information system widely, security has remained one hard-hitting area for computers as well as networks. In information protection, Intrusion Detection System (IDS) is used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining is an efficient artifice that can be applied to intrusion detection to ascertain a new outline from the massive network data as well as it use to reduce the strain of the manual compilations of the normal and abnormal behaviour patterns. This work reviews the present state of data mining techniques and compares various data mining techniques used to implement an intrusion detection system such as, Support Vector Machine, Genetic Algorithm, Neural network, Fuzzy Logic, Bayesian Classifier, K- Nearest Neighbour and decision tree Algorithms by highlighting the advantages and disadvantages of each of the techniques