Date of Award

3-2019

Culminating Project Type

Thesis

Degree Name

Information Assurance: M.S.

Department

Information Assurance and Information Systems

College

Herberger School of Business

First Advisor

Jim Chen

Second Advisor

Jie Meichsner

Third Advisor

Mark Schmidt

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Machine Learning, Deep Learning, Database Auditing, Artificial Neural Networks, A. Daghighi

Abstract

Data auditing is a fundamental challenge for organizations who deal with large databases. Databases are frequently targeted by attacks that grow in quantity and sophistication every day, and one-third of which are coming from users inside the organizations. Database auditing plays a vital role in protecting against these attacks. Native features in data base auditing systems monitor and capture activities and incidents that occur within a database and notify the database administrator. However, the cost of administration and performance overhead in the software must be considered. As opposed to using native auditing tools, the better solution for having a more secure database is to utilize third-party products. The primary goal of this thesis is to utilize an efficient and optimized deep learning approach to detect suspicious behaviors within a database by calculating the amount of risk that each user poses for the system. This will be accomplished by using an Artificial Neural Network as an enhanced feature of analyzer component of a database auditing system. This ANN will work as a third-party product for the database auditing system. The model has been validated in order to have a low bias and low variance. Moreover, parameter tuning technique has been utilized to find the best parameters that would result in the highest accuracy for the model.

Comments/Acknowledgements

Foremost, I would like to express my sincere gratitude to my advisor Professor Jim Chen for his motivation, support, enthusiasm and continuous encouragement. He is an outstanding professor and a great advisor and I am grateful to have the opportunity to work under his supervision. I would like to thank Dr. Jie Meichsner and Dr. Mark Schmidt for their invaluable support, hard questions, and insightful comments.

I would like to thank my family who helped me find my way through hardship, make right decisions, and providing support for me throughout my life.

Last but not the least, I would like to thank Dr. Jim A. Rakhshani who has been my role model, and I am thankful for his continuous support, for his patience and the motivation that he provided throughout my study in United States.

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