Application of Change Point Outlier Detection Methods in Real Time Intrusion Detection

Recent years has shown a growing interest in the development of change detection techniques for the analysis of Intrusion Detection. Current research shows that change detection methods can be used for a wide range of real time applications. Detecting the changes by observing data collected at different times is one of the most important applications of network security because they can provide analysis of short interval on global scale. Research in exploring change detection techniques for medium/high network data can be found for the new generation of very high resolution data. The advent of new technologies has greatly increased the ability to monitor and resolve the details of changes in order to analyze better. Analyzing large amount of data is still a new challenge. The data need to be analyzed and corrected for registration and classification errors for identifying frequently changing trend. In this research paper we have proposed a unified and novel approach for Intrusion Detection System (IDS) which embeds a Change Detection Algorithm with Data Mining (DM) technique. IDS are considered as a system integrated with intelligent subsystems, which completes the distributed solution procedure on the basis of exchanging large data and information. The goal is to learn more effectively from the model. The knowledge developed automatically adjusts to the changes as well as threshold while minimizing the false alarm rate and timely detection. A hybrid approach for improving the performance of detection algorithm by building more intelligence to the system is proposed using Support Vector Machine (SVM). The results are properly substantiated for better effectiveness, system security and flexibility.