Map-Reduce based Ensemble Intrusion Detection System with Security in Big Data

Abstract

In Many domains, data is increasing day by day. Moreover, the large amount of data creates the best challenges for the users. Big data is one of the high focus domains in data science. In many organizations, big data plays a major role in processing large datasets and extracts useful information from the data. Security becomes more important to all the domains and applications. Many applications use different types of security approaches to secure sensitive data from attackers. Many real-time applications are providing security for applications such as banking, trading, and e-commerce applications using better security algorithms. Processing a large amount of real-time data gets a lot of benefits to analyzing the threats in cyber-security systems. The data is collected from online real-time sources and this cyber security data consists of network information, sensor data, threat information, analysis of intrusion detection, and identifying the sensitive data in real-time applications. In this collected information various vulnerabilities and attacks are becoming prevalent and develop security solutions accordingly. In this paper, the Map-reduce-based Ensemble Intrusion Detection System (MR-EIDS) is developed to detect intruders and attackers from the real-time datasets. Two benchmark datasets are used to analyze the intrusion data. The proposed system analyzes the dataset and finds interesting patterns in the datasets. Performance is also measured by showing improved results.