PROJECT TITLE :
Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing - 2018
Virtualized infrastructure in cloud computing has become an enticing target for cyberattackers to launch advanced attacks. This Project proposes a unique huge data based mostly security analytics approach to detecting advanced attacks in virtualized infrastructures. Network logs plus 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 ways. Next, determination of attack presence is performed through two-step machine learning, specifically logistic regression is applied to calculate attack's conditional probabilities with respect to the attributes, and belief propagation is applied to calculate the assumption living of an attack primarily based on them. Experiments are conducted to guage the proposed approach using well-known malware also compared with existing security techniques for virtualized infrastructure. The results show that our proposed approach is effective in detecting attacks with minimal performance overhead.
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