Dr. Diogo Oliveira

• Position: Assistant Professor
• Department: Cybersecurity and Information Technology
• Office Location: Building 4, Room 243
• dnunesdeoliveira@uwf.edu
• Campus: 850.474.2582

Biography

Dr. Oliveira received his PhD in Electrical Engineering from the USF Department of Electrical Engineering in 2018 and his MS in Computer Engineering from the Federal University of Goias (Brazil) in 2009. He is currently an Assistant Professor at the University of West Florida. Diogo has extensive experience in various areas under the cybersecurity umbrella, such as reverse engineering, malware analysis, ethical hacking, digital forensics, and resilient large-infrastructure networks. 

Degrees & Institutions

• Ph.D. Electrical Engineering, University of South Florida
• M.S. Computer Engineering, Federal University of Goias

Research

Dr. Oliveira's research agenda brings together interests in the areas of machine learning (ML), cybersecurity, and network infrastructure/resilience. Specifically, his research studies fall within the areas of malware analysis, threat analysis, computer forensics, penetration testing, machine learning, software-defined networking, network function virtualization, and network survivability. Overall, Diogo focuses on mixing theoretical knowledge with technical skills to propose quantitative and qualitative methods to answer questions in the realm of the aforementioned areas. Specifically, his current studies suggest the analysis and deployment of machine learning models for optimal detection and classification of cyber-attacks.

Also, he proposes sensitivity analysis and dataset preprocessing techniques to promote optimal machine learning solutions. Previously to his current research, Dr. Oliveira conducted studies in network infrastructure resilience, survivability, and performance, also leveraging machine learning and optimization models.

Publications

Journals Papers

S. Jagatheesaperumal, M. Rahouti, M. Aledhari, A. Hafid, D. Oliveira, H. Drid, R. Admin, “Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks”, Computing and AI Connect, Volume 1, Jan. 2025.

A. Aldalbahi, M. Jasim, F. Shahabi, A. Mazin, N. Siasi, D. Oliveira, “Deep Learning for Primary Sector Prediction in FR2 New Radio Systems”, IEEE Access, Volume 9, Issue 18, Dec. 2021.

Conference Papers

D. Oliveira, M. Ray, R. Lomotey, M. Rahouti, “Memory Feature Engineering for Performance-Gain in Obfuscated Malware Detection Using Machine Learning and Sensitivity Analysis”, 2024 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Victoria, Canada, August 2024.

A. Alfatemi, D. Oliveira, M. Rahouti, A. Hafid, N. Ghani, “Precision DDoS Detection through Gaussian Noise-Augmented Neural Networks”, 15th International Conference on Network of the Future, Barcelona, Spain, Oct. 2024.

Y. Shein, J. Qi, F. Martinez, M. Rahouti, F. Hsu, D. Oliveira, “Comparative Analysis of Machine Learning and Traditional Forecasting Methods in Bitcoin Price Prediction”, 2024 6th International Conference on Blockchain Computing and Applications, Dubai, UAE, November 2024.

A. Hafid, M. Ebrahim, M. Rahouti, D. Oliveira, “Cryptocurrency Price Forecasting Using XBoost Regressor and Technical Indicators”, 2024 IEEE International Performance Computing and Communications Conference, Orlando, FL, Nov. 2024.

L. Benaddi, C. Quaddi, J. Abdeslam, S. Rachid, B. Ouchao, M. Rahouti, D. Oliveira, “An Analytical Study on the Evolution and Impact of Chatbots in Tourism Over the Past Decade”, 2024 IEEE International Performance Computing and Communications Conference, Orlando, FL, Nov. 2024.