Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing

Thu Yein Win

doi:10.1109/TBDATA.2017.2715335

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Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing

2018-03-01

Thu Yein Win *

Huaglory Tianfield

DOI：10.1109/TBDATA.2017.2715335

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Abstract

En 中文

Virtualized infrastructure in cloud computing has become an attractive target for cyberattackers to launch advanced attacks. This paper proposes a novel big data based security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs as well as user application logs collected periodically from the guest virtual machines (VMs) are stored in the Hadoop Distributed File System (HDFS). Then, extraction of attack features is performed through graph-based event correlation and MapReduce parser based identification of potential attack paths. Next, determination of attack presence is performed through two-step machine learning, namely logistic regression is applied to calculate attack's conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the belief in existence of an attack based on them. Experiments are conducted to evaluate the proposed approach using well-known malware as well as in comparison with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.

Keywords:

Virtualized infrastructure

virtualization security

cloud security

malware detection

rootkit detection

security analytics

event correlation

logistic regression

belief propagation

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