Abstract
Nowadays, cyber-targeted attacks such as APT are rapidly growing as a social and national threat. As an intelligent cyber-attack, the cyber-targeted attack infiltrates the target organization or enterprise clandestinely using various methods and causes considerable damage by making a final attack after long-term and through preparations. Detecting these attacks requires collecting and analyzing data from various sources (network, host, security equipment) over the long haul. Therefore, this paper describes the system that responds to the cyber-targeted attack based on Big Data and a method of abnormal behavior detection among the cyber-targeted attack detection techniques provided by the proposed system. Specifically, the proposed system analyzes faster and precisely various logs and monitoring data that have been discarded using Big Data storage and processing technology; it also provides integrated security intelligence technology through data correlation analysis. In particular, abnormal behavior detection using MapReduce is effective in analyzing large-scale host behavior monitoring data.
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Acknowledgments
This research was funded by the MSIP (Ministry of Science, ICT and Future Planning), Korea in the ICT R&D Program 2013 [Cyber-targeted attack recognition and trace-back technology based on the long-term historic analysis of multi-source data].
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© 2014 Springer Science+Business Media Dordrecht
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Kim, H., Kim, I., Chung, TM. (2014). Abnormal Behavior Detection Technique Based on Big Data. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_66
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DOI: https://doi.org/10.1007/978-94-017-8798-7_66
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