Published Paper Details:

Intrusion Detection System (IDS) is meant to be a software application which monitors the network orsystem activities and finds if any malicious operations occur. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. We can build Intrusion detection system model to find attacks on system and can improve the system using captured data. By applying feature selection approach in machine learning, the NSLKDD data set obtained can be reduced and also can improve the intrusion detection using the captured data. By machine learning techniques, we can increase number of new unseen attacks the system of intrusion detection can be developed .Though efficient adaptive methods like various techniques of machine learning can result in higher detection rates, lower false alarm rates and reasonable computation and communication cost. With the use of data mining can result in frequent pattern mining, classification, clustering and mini data stream. This survey paper describes a focused literature survey of machine learning and data mining methods for cyber in support of intrusion detection