Research Article
Security Analytics and Benchmarking Log Aggregation in the Cloud
@ARTICLE{10.4108/eai.11-4-2018.154464, author={Purvi Pathak and Kumar Rangasamy and Theophilus Selvaraj}, title={Security Analytics and Benchmarking Log Aggregation in the Cloud}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={3}, number={11}, publisher={EAI}, journal_a={CS}, year={2018}, month={4}, keywords={cloud, log aggregation, security, analytics.}, doi={10.4108/eai.11-4-2018.154464} }
- Purvi Pathak
Kumar Rangasamy
Theophilus Selvaraj
Year: 2018
Security Analytics and Benchmarking Log Aggregation in the Cloud
CS
EAI
DOI: 10.4108/eai.11-4-2018.154464
Abstract
With increase in popularity of Cloud computing, most organizations are moving towards the Cloud. The main concern for these organizations when migrating to the Cloud is securing their data in the Cloud. There are security measures that can be deployed to address the risk the organization faces to the security threats posed within the Cloud. This project illustrates how the problem can be solved using data protection techniques and security analytics of the log data within the Cloud deployment. In PaaS implementation of Cloud, the customer and the Cloud vendor has a shared responsibility model and the project will discuss what customer can do for their responsibility in the areas highlighted above. Data is of paramount importance to any organization and protection of data becomes more complex in a Cloud offering as the storage is located off premise. Like any other environment devices, servers and applications in Cloud produce logs that can be aggregated and analyzed to identify security anomalies. Comparison of various log aggregation tools can give a detailed idea about what tool is better. Two log aggregation tools Splunk and the Elastic stack have been compared in this project. A combination of the above described strategies can address and point on various security risks, and help reduce the risk of the organization to a significant degree.
Copyright © 2018 Purvi Pathak et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.