Term of Award

Spring 2017

Degree Name

Master of Science in Applied Engineering (M.S.A.E.)

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Committee Chair

Hayden Wimmer

Committee Member 1

Lei Chen

Committee Member 2

Susan Williams


In today’s technologically advanced world, crimes are committed via the internet and with smartphones. Whether it is stealing personal health information, money, or plotting terrorist activities, the internet and mobile devices play a crucial role and are often exploited by criminals as a medium for malicious activities. To protect their citizens from cybercrimes, governments attempt to keep pace with technology by enacting new laws and regulations. In the US, the government instituted the Health Insurance Portability and Accountability Act, or HIPAA, to safeguard protected personal health information. To strengthen the government efforts, our first work instantiates and demonstrates a method to secure streaming of data via RC4 (Rivest Cipher 4) encryption while improving its security by incorporating variable length cipher strength via a proposed Pseudo-Random Generation Algorithm, or PRGA, key rotation method. To facilitate this method, multiple keys can be transmitted over disparate mediums or channels in an extension to RC4, dubbed RC4 modified (RC4m). RC4m can be employed to secure data transmitted via mobile devices.

On the other hand, it is also important to develop the capacity to analyze mobile data. Terrorism and cybercrime are a worldwide concern with mobile devices providing a vehicle for malevolent activities. The number of smartphone users has increased rapidly over the last six years, from 32 million in 2010 to 207 million in 2016. This pervasiveness and transformation had turned smartphones into a goldmine for forensics research; however, modern tools and techniques are not adequate for the vast amounts of data generated by mobile devices. To address this gap, we propose and instantiate a Big Forensic Data Framework. We extract mobile data using the Encase Forensic Toolkit and process and aggregate the data utilizing big data technologies, namely Hadoop and MapReduce. Finally, we employ social network analysis on the aggregated data to reveal relationships between contacts extracted from the mobile devices.

Research Data and Supplementary Material