Increasing efficiency of intrusion detection system using stream data mining classification

Software Engineering

Data mining is the process of extracting knowledge or information from the previously known and comprehensive datasets for the future decision making. Data mining is the not only withdraw of hidden predictive information but also a strong technology that has great perspective to companies to focus on the most important data in their data repository. Data is growing day by day and this growth has created so many challenges in data mining. The improved technology by World Wide Web is streaming data. The streaming data come into the picture with its challenges and it is known as the data which change with time and update its value. In the sense of security perspective, as the most of the data is streaming in nature, there are so many challenges need to face. The Intrusion Detection System (IDS) work in the supposition of detecting the intruders to protect the respective system. Due to the importance of system’s safety measure the research in data stream mining and Intrusion detection system gained high attraction. In this paper, we represent the mechanism to improve the efficiency of the IDS using different streaming data mining classification technique. We apply four selected stream data classification algorithms on NSL-KDD datasets and compared their results. Based on the comparative analysis of their results best method is found out for efficiency improvement of IDS.

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