INTRUSION DETECTION USING BIG DATA ANALYSIS

T. Jhansi Rani

Abstract


Security is always an important issue especially in the case of computer network which is used to transfer personal/confidential information’s, ecommerce and media sharing. Data in computer networks is growing rapidly; the analysis of these large amounts of data to discover anomaly fragments has to be done within a reasonable amount of time. Recently, threat of previously unknown cyber-attacks is increasing because existing security systems are not able to detect them. The goal of recent hacking attacks has changed from leaking information and destruction of services to attacking large-scale systems such as critical infrastructures and state agencies. To defend against these attacks, which can not detected with existing intrusion detection algorithm we propose a new model based on big data analysis. Previous intrusion detection algorithm detects predefined attacks. This kind of intrusion detection system is called as signature based intrusion detection system. Big data analysis technique can extract information from Varity of sources to detect future attack. Big data analysis framework use MapReduced intrusion detection system based on clustering algorithm.

Keywords


Hadoop, MapReduce, Targeted attacks, Intrusion detection system, C-Means Clustering, Support Vector Machine (SVM).

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