Australian Information Security Management Conference

Document Type

Conference Proceeding

Publisher

SRI Security Research Institute, Edith Cowan University, Perth, Western Australia

Comments

This paper was originally presented at The Proceedings of [the] 13th Australian Information Security Management Conference, held from the 30 November – 2 December, 2015 (pp. 73-80), Edith Cowan University Joondalup Campus, Perth, Western Australia.

Abstract

Recently, cyber-targeted attacks such as APT (Advanced Persistent Threat) are rapidly growing as a social and national threat. It is an intelligent cyber-attack that infiltrates the target organization and enterprise clandestinely using various methods and causes considerable damage by making a final attack after long-term and through preparations. These attacks are threatening cyber worlds such as Internet by infecting and attacking the devices on this environment with the malicious code, and by destroying them or gaining their authorities. Detecting these attacks requires collecting and analysing data from various sources (network, host, security equipment, and devices) over the long haul. Therefore, we propose the method that can recognize the cyber-targeted attack and detect the abnormal behavior based on Big Data. The proposed approach analyses faster and precisely various logs and monitoring data using Big Data storage and processing technology. In particular, we evaluated that the suspicious behavior analysis using MapReduce is effective in analysing large-scale behavior monitoring and log data from various sources.

DOI

10.4225/75/57b69d1ed938e

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