Intrusion Detection System: A Survey Using Data Mining and Learning Methods

Abstract

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 applied to intrusion detection to ascertain a new outline from the massive network data as well as it used to reduce the strain of the manual compilations of the normal and abnormal behavior patterns. Intrusion Detection System (IDS) is an essential method to protect network security from incoming on-line threats. Machine learning enable automates the classification of network patterns. This piece of writing 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 Neighbor and decision tree Algorithms by highlighting a advantage and disadvantages of each of the techniques. This paper review the learning and detection methods in IDS, discuss the problems with existing intrusion detection systems and review data reduction techniques used in IDS in order to deal with huge volumes of audit data. Finally, conclusion and recommendation are included.

Keywords: Classification, Data Mining, Intrusion Detection System, Security, Anomaly Detection, Types of attacks, Machine Learning Techniques