Abstract

The purpose of this research is to analyze the security of next-generation big data processing (BDP) and examine the feasibility of applying advanced security features to meet the needs of modern multi-tenant, multi-level data analysis. The research methodology was to survey of the status of security mechanisms in BDP systems and identify areas that require further improvement. Access control (AC) security services were identified as priority area, specifically Attribute Based Access Control (ABAC). The exemplar BDP system analyzed is the Apache Hadoop ecosystem. We created data generation software, analysis programs, and posted the detailed the experiment configuration on GitHub. Overall, our research indicates that before a BDP system, such as Hadoop, can be used in operational environment significant security configurations are required. We believe that the tools are available to achieve a secure system, with ABAC, using Apache Ranger and Apache Atlas. However, these systems are immature and require verification by an independent third party. We identified the following specific actions for overall improvement: consistent provisioning of security services through a data analyst workstation, a common backplane of security services, and a management console. These areas are partially satisfied in the current Hadoop ecosystem, continued AC improvements through the open source community, and rigorous independent testing should further address remaining security challenges. Robust security will enable further use of distributed, cluster BDP, such as Apache Hadoop and Hadoop-like systems, to meet future government and business requirements.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Zou, Cliff

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Electrical and Computer Engineering

Degree Program

Computer Engineering

Format

application/pdf

Identifier

CFE0009067; DP0026400

URL

https://purls.library.ucf.edu/go/DP0026400

Language

English

Release Date

May 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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