Vol: 61(75) No: 1 / March 2016 |
Optimized Automated Anomalies Detection in Cloud Computing Infrastructures
Department of Computer Science, Military Technical Academy, Bucharest, Romania, e-mail: firstname.lastname@example.org
Department of Computer Science, Military Technical Academy, Bucharest, Romania, e-mail: email@example.com
Victor Valeriu Patriciu
Department of Computer Science, Military Technical Academy, Bucharest, Romania, e-mail: firstname.lastname@example.org
Intel Corporation, Bucharest, Romania, e-mail: email@example.com
Keywords: cloud computing; data forensic; anomaly detection framework; distributed computing
The need of knowing exactly where and how a piece of data is stored and processed in a datacenter infrastructure is important in our days due to the increased number of cyber attacks that are constantly triggered. For this, we need a full picture of what is doing on, and a centralized system that constantly collects, analyzes and corellates information from the physical and virtual instances in order to detect known anomalies and any other usage pattern that can lead to a security breach. In this paper we present an optimized way to monitor virtual instances that are running in a particular datacenter. We will talk about the architecture and the way in which we used all the collected information to train our automated anomalies learning modules. We also present some implementation details and results taken from our experimental setup.
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