Efficient Intrusion Detection System Using Stream Data Mining Classification Technique

Recent emerging growth of data created so many challenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the most of the data is streaming in nature, there are so many challenges need to face in the sense of security perspective. Intrusion Detection System (IDS) works in the supposition of detecting the intruders to protect the respective system. The research in data stream mining & Intrusion detection system gained high attraction due to the importance of system's safety measure. Algorithms, systems & frameworks that address security challenges have been developed over the past years. In this paper, we present the mechanism to improve the efficiency of the IDS using streaming data mining technique. We apply four selected stream data classification algorithms on NSL-KDD datasets and compare their results. Based on the comparative analysis of their results best method is found out for efficiency improvement of IDS.