Intrusion Detection system has become the main research focus in the area of information security. Last few years have witnessed a large variety of technique and model to provide increasingly efficient intrusion detection solutions. Traditional Network IDS are limited and do not provide a comprehensive solution for these serious problems which are causing many types of security breaches and IT service impacts. They search for potential malicious abnormal activities on the network traffics; and sometimes succeed to find true network attacks and anomalies (true positive). However, in many cases, systems fail to detect malicious network behaviors (false negative) or they fire alarms when there is nothing wrong in the network (false positive). In accumulation, they also require extensive and meticulous manual processing and interference. The authors advocate here applying Data Mining (DM) techniques on the network traffic data is a potential solution that helps in design and development of a better efficient intrusion detection system. Data mining methods have been used to build the automatic intrusion detection systems. The central idea is to utilize auditing programs to extract the set of features that describe each network connection or session, and apply data mining programs, to learn that capture intrusive and non-intrusive behavior. In this research paper, the authors are focusing on Data Mining based intrusion detection system.