Over the past several years, the Internet environment has become more complex and un-trusted. Enterprise networked systems are inevitably exposed to the increasing threats posed by hackers as well as malicious users internal to a network. Intrusion Detection System (IDS) technology is one of the important tools used now-a-days, to detect such threats, which is a predictable element of the computer network system. Various IDS techniques has been proposed, which identifies and alarms for such threats or attacks. Data mining provides a wide range of techniques to classify these attacks. Today's IDS faces a number of key challenging issues. The challenges like detect malicious activities of the large amount of network traffic. The main challenging if some attacks were sneaking as normal connection. [1] This paper provides a proposed system which performs well when compare to other IDS also introduce a comparative study of strong and weak points with other system on the attack detection rate of these existing classification techniques.

Intrusion detection data mining network security