DISSERTATION DEFENSE

Author : Theppatorn Rhujittawiwat

Advisor : Dr. Csilla Farkas

Date : Oct 31, 2022

Time :  2:00 - 3:30 pm

Meeting Link

Abstract

In this dissertation, we studied how an adversary could attack databases and how the system could prevent or recover from such an attack. Our motivation to improve the current security capabilities of database management systems. We provided better recovery capabilities of database management systems by incorporating data provenance. We also expand our study to express security and privacy needs of data in the Internet of Things (IoT) environments such as a smart home environment. For this, we proposed a stream data security model to theoretically represent the data in the IoT network. We built a dynamic authorization model on our context-aware architecture and stream data model. We demonstrated the capabilities of our dynamic security policy to address security needs due to the changes in the context. Furthermore, we demonstrated the applicability of our approach by implementing our framework in a smart home IoT network. For our proof-of-concept implementation, we used a commercial and open-source home automaton software. Our approach to improve the system is expanding it by incorporating third party applications, such as a dynamic access control engine. We aim to incorporate a logic reasoner into smart home automaton to provide situation-aware capabilities to the system in this study.