Preprint Article Version 1 This version is not peer-reviewed

Big Data Log-Based Correlation Analysis Profiling Auto Generation Model

Version 1 : Received: 11 April 2018 / Approved: 11 April 2018 / Online: 11 April 2018 (08:39:02 CEST)

How to cite: Sohn, D.; Huh, S.; Lee, T.; Kwak, J. Big Data Log-Based Correlation Analysis Profiling Auto Generation Model. Preprints 2018, 2018040144 (doi: 10.20944/preprints201804.0144.v1). Sohn, D.; Huh, S.; Lee, T.; Kwak, J. Big Data Log-Based Correlation Analysis Profiling Auto Generation Model. Preprints 2018, 2018040144 (doi: 10.20944/preprints201804.0144.v1).

Abstract

The number of SIEM introduction is increasing in order to detect threat patterns in a short period of time with a large amount of structured/unstructured data, to precisely diagnose crisis to threats, and to provide an accurate alarm to an administrator by correlating collected information. However, it is difficult to quickly recognize and handle with various attack situations using a solution equipped with complicated functions during security monitoring. In order to overcome this situation, new detection analysis process has been required, and there is an effort to increase response speed during security monitoring and to expand accurate linkage analysis technology. In this paper, reflecting these requirements, we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats requirements. we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats.

Subject Areas

big data; SIEM; correlation analysis; cyber crime profiling

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