A Survey on Building Intrusion Detection System Using Data Mining Framework

Recently, network attacks have increased to a greater extent. Hackers and intruders can produce several successful efforts to cause the crash of the networks and web services by illegal intrusion. New threats and interrelated solutions to avoid these threats are budding jointly with the secured system evolution. So, Intrusion Detection System (IDS) has become an active area of research in the field of network security. The optimization of IDS becomes an attractive domain due to the security audit data as well as complex and active properties of intrusion behaviors. The main purpose of IDS is to protect the resources from threats. Intrusion Detection System examines and calculates the user behavior, and then these behaviors will be considered an attack or a normal behavior. Intrusion detection systems have been integrated with data mining approaches to identify intrusions. There are various data mining approaches such as classification tree, Support Vector Machines, etc., used for intrusion detection. In this paper, thorough investigations have been done on the existing data mining approaches to detect intrusions.