Dr. Armon Barton is an Assistant Professor of Computer Science at the Naval Postgraduate School (NPS), where his research focuses on artificial intelligence, machine learning, cybersecurity, and security and privacy. His primary research interests include adversarial and secure machine learning, computer vision, anonymous communications, network traffic analysis, and the development of robust AI systems for defense applications. He has served as principal investigator on research sponsored by Army Network Enterprise Technology Command (NETCOM) and the Naval Research Program, addressing problems such as cyber threat detection, network traffic analysis, reinforcement learning for naval maneuvering, radar signal analysis, and adversarial robustness of computer vision systems. His research has been published in venues including ACM Transactions on Privacy and Security, Transactions on Machine Learning Research (TMLR), Proceedings on Privacy Enhancing Technologies Symposium (PETS), USENIX Security, and IEEE conferences.

Dr. Barton received his Ph.D. in Computer Science from the University of Texas at Arlington, where his doctoral research focused on defending neural networks against adversarial examples. Before joining NPS, he was a Research Scientist at Lockheed Martin, where he developed deep learning methods for wide-area motion imagery and satellite imagery analysis. At NPS, he works closely with graduate students and defense partners to connect fundamental AI and cybersecurity research with operationally relevant problems. He is particularly interested in developing AI systems that remain reliable and secure in adversarial environments and in exploring emerging areas of AI and computing. His broader research goal is to anticipate vulnerabilities in emerging intelligent systems and develop methods that enable their secure and trustworthy use in national security applications.

RESEARCH PUBLICATIONS

Drusinsky, D., Barton, A., Litton, M., & Zimmer, J. (2026). From Shortest Path to Safest Path. IEEE Computer, 59(07), 132-145.

Barton, A., Wright, M., Shahriyar, S. A., Jatho, E., Rahman, M. S., Gangadhara, K. G., & Ming, J. (2026). PadNet: Defending Neural Networks Against Adversarial Examples. ACM Transactions on Privacy and Security, 29(2), 1-26. Impact Factor: 3.4

Jatho, E. W., Barton, A., Wright, M., & McClure, P. (2026) Overcoming Open-Set Approaches to Adversarial Defense. Transactions on Machine Learning Research (TMLR). h-index 41

Barton, A., Walsh, T., Imani, M., Ming, J., & Wright, M. (2025). Predictor: A Global, Machine Learning Approach to Tor Path Selection. ACM Transactions on Privacy and Security, 28(3), 1-31. Impact Factor: 3.4

Walsh, T., Barton, A., & Kölsch, M. (2025). Improved Open-World Fingerprinting Increases Threat to Streaming Video Privacy but Realistic Scenarios Remain Difficult. Proceedings on Privacy Enhancing Technologies (PoPETs). h5-index: 56

Clark, C., & Barton, A. (2025). GTS Attack: Finding Adversarial Examples With Greedy Tree Search. IEEE Computer, 58(07), 138-146.

Hayden, B., Walsh, T., & Barton, A. (2024). Defending Against Deep Learning-Based Traffic Fingerprinting Attacks with Adversarial Examples. ACM Transactions on Privacy and Security, 28(1), 1-23. Impact Factor: 3.4

Akers, M., & Barton, A. (2024). Forming Adversarial Example Attacks Against Deep Neural Networks with Reinforcement Learning. IEEE Computer, 57(1), 88-99.

Rowe, N. C., Green, J. J., Benn, A. S., Drew, S. K., Heinen, C. W., Bixler, R. E., ... & Barton, A. C. (2025). Game-based testing for active cyberdefense and cyberdeception. Cybersecurity: Cyber Defense, Privacy and Cyber Warfare, 4, 1.

Armon Barton, Mohsen Imani, Jiang Ming, and Matthew Wright. Towards Predicting Efficient and Anonymous Tor Circuits. In Proceedings of the 27th USENIX Security Symposium (USENIX Security'18), Baltimore, MD, USA, August 15-17, 2018. (Acceptance ratio: 19.1%)

Mohsen Imani, Armon Barton and Matthew Wright. Guard Sets in Tor using AS Relationships. In Proceedings of the 18th Privacy Enhancing Technologies Symposium (PETS’18), Barcelona, Spain, July 24-27, 2018. (Acceptance ratio: 17.3%)

Armon Barton, Mohsen Imani, and Matthew Wright. DeNASA: Destination-Naive AS-Awareness in Anonymous Communications. In Proceedings of the 16th Privacy Enhancing Technologies Symposium (PETS’16), Darmstadt, Germany, July 19-22, 2016. (Acceptance ratio: 23.8%)

Mohr, N., Shaffer, A., Singh, G., & Barton, A. (2026, March). A Hybrid Machine Learning Approach for Red Team Log Analysis. In Proceedings of the 21st International Conference on Cyber Warfare and Security. Academic Conferences and publishing limited.

Shahriyar, S. A., Wright, M., & Barton, A. (2025, May). Ivory: Adversarial Purification of Obfuscated Faces to Extract Soft-Biometrics using Diffusion Transformers. In 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG) (pp. 1-10). IEEE.

Straughn, M., Barton, A., & Allen, B. (2024, July). Detecting Malware Traffic with Graph Neural Networks. In World Congress in Computer Science, Computer Engineering & Applied Computing (pp. 3-21). Cham: Springer Nature Switzerland.

Walsh, T., Thomas, T., & Barton, A. (2024, May). Exploring the Capabilities and Limitations of Video Stream Fingerprinting. In 2024 IEEE Security and Privacy Workshops (SPW) (pp. 28-39). IEEE.

Coble, J., Barton, A., Darken, C., & Black, S. (2023, December). Optimizing Naval Movement Using Deep Reinforcement Learning. In 2023 International Conference on Machine Learning and Applications (ICMLA) (pp. 400-407). IEEE.

Green, J. J., Drew, S. K., Heinen, C. W., Bixler, R. E., & Barton, A. C. (2023, July). Evaluating a Planning Product for Active Cyberdefense and Cyberdeception. In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 2451-2456). IEEE.

TEACHING

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2021

CS3315 Introduction to Machine Learning and Big Data, Section 2, Q1 2021

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2022

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2023

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2024

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2025

CS3315 Introduction to Machine Learning and Big Data, Section 2, Q1 2025

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2026

CS3315 Introduction to Machine Learning and Big Data, Section 1, Q1 2027

CS3021 Introduction to Data Structures and Intermediate Programming, Section 1, Q4 2021

CS3310 Artificial Intelligence, Section 1, Q3 2022

CS3310 Artificial Intelligence, Section 1, Q1 2023

CS3310 Artificial Intelligence, Section 1, Q1 2024

CS3310 Artificial Intelligence, Section 1, Q3 2024

CS3310 Artificial Intelligence, Section 2, Q3 2024

CS3310 Artificial Intelligence, Section 1, Q3 2025

CS3310 Artificial Intelligence, Section 1, Q3 2026

CS4342 Adversarial and Secure Machine Learning, Section 1, Q3 2026

CS4342 Adversarial and Secure Machine Learning, Section 1, Q1 2027

THESIS ADVISING

Drew, Sasha Keshia, TESTING DECEPTION WITH A COMMERCIAL TOOL SIMULATING CYBERSPACE, MS Applied Cyber Operations, 26Mar21

Heinen, Charles William, TESTING DECEPTION WITH A COMMERCIAL TOOL SIMULATING CYBERSPACE, MS Applied Cyber Operations, 26Mar21

Kim, Elissa Soojin, PREDICTING THE UNKNOWN: MACHINE LEARNING TECHNIQUES FOR VIDEO FINGERPRINTING ATTACKS OVER TOR, MS Cyber Systems and Operations, 17Dec21

Hayden, Blake Alexander, DEFENDING AGAINST DEEP LEARNING-BASED VIDEO FINGERPRINTING ATTACKS WITH ADVERSARIAL EXAMPLES, MS Computer Science, 17Jun22

Calnan, Michael Claypool, MULTI-DIMENSIONAL PROFILING OF CYBER THREATS FOR LARGE-SCALE NETWORKS, MS Computer Science, 23Sep22

Sanchez Garcia, Yvonne, APPLYING NATURAL LANGUAGE PROCESSING (NLP) TO ASSESS HEALTH OF MARINE CORPS CULTURE, MS Computer Science, 23Sep22

Sweeney, Matthew Kevin, DECISION MODEL IMPLEMENTATION IN THE GLOBAL INFORMATION NETWORK ARCHITECTURE, MS Computer Science, 23Sep22

DeRidder, Daniel Steven, ATTACKING NEURAL NETWORKS WITH HIGH ENTROPY INPUT SAMPLING, MS Computer Science, 23Sep22

Slaughter, Jacob Ward, ON EUCLIDEAN NETWORKS FOR IMPROVING CLASSIFICATION ACCURACY, MS Computer Science, 24Mar23

Kallis, Shaun, DETECTING AND DEFENDING AGAINST DIFFERENT FAMILIES OF ADVERSARIAL EXAMPLE ATTACKS, MS Computer Science, 24Mar23

Menon, Tarun, ANOMALY DETECTION ON FLOWS AND INCOMING PACKETS WITH GAUSSIAN MIXTURES, MS Computer Science, 24Mar23

Akers, Matthew Douglas, FORMING ADVERSARIAL EXAMPLE ATTACKS AGAINST DEEP NEURAL NETWORKS WITH REINFORCEMENT LEARNING, MS Computer Science, 24Mar23

Duhe', Paul Howard, REMOVING THE MASK: VIDEO FINGERPRINTING ATTACKS OVER TOR, MS Computer Science, 24Mar23

Andrianopoulos, Georgios, EXPLORING NEURAL NETWORK DEFENSES WITH ADVERSARIAL MIXUP, MS Computer Science, 24Mar23

Coble, Joseph Robert, OPTIMAL NAVAL MOVEMENT SIMULATION WITH REINFORCEMENT LEARNING AI AGENTS, MS Computer Science, 16Jun23

Straughn, Matthew Nolan, CLASSIFYING TCP NETWORK TRAFFIC FLOWS VIA TRAFFIC INTERACTION GRAPHS AND MACHINE LEARNING, MS Computer Science, 22Sep23

Thomas, Trevor John, OVERCOMING TOR: AN ANALYSIS OF VIDEO FINGERPRINTING ATTACKS WITH MACHINE LEARNING, MS Computer Science, 22Sep23

Clark, Christopher David, A* ATTACK: A NOVEL PATH-FINDING APPROACH TO ADVERSARIAL EXAMPLES, MS Computer Science, 22Sep23

Huang, Alexander, NEURAL NETWORK MODEL INTERPRETABILITY FOR COMPUTER NETWORK OPERATIONS AND DEFENSE, MS Computer Science, 15Dec23

Tan, Swee Khoon, USING DCGAN TO GENERATE SYNTHETIC PACKET FLOWS FOR THREAT DETECTION, MS Computer Science, 27Sep24

Falk, Theodore Beaureguard, TOR NETWORK VIDEO FINGERPRINTING OVER RESIDENTIAL WI-FI AND CONGESTED NETWORK INTERFACES, MS Computer Science, 18Dec24

Thornton, Cory Warren, STUDY ON JUNIPER SMART SESSION ROUTER NETWORK TRAFFIC CHARACTERIZATION UTILIZING MACHINE LEARNING, MS Computer Science, 28Mar25

Ang, Paul Lovett, ATTACKING MULTI-LABEL AUDIO CLASSIFICATION WITH GREEDY TREE SEARCH, MS Computer Science, 28Mar25

Nkuako, Kojo Amponsah, FEDERATED LEARNING IN CYBERSECURITY: PERFORMANCE, ROBUSTNESS, AND ADVERSARIAL THREATS, MS Computer Science, 18Jun25

Zimmer, Jason Kyle, SAFE NAVIGATION: ROUTE PLANNING IN CONTESTED ENVIRONMENTS USING SHORTEST PATH ALGORITHMS, MS Computer Science, 26Sep25

DelValle, Antolin David, PASSIVE RADAR TRACK CLUSTERING: HIGHER FIDELITY OF TARGET IDENTIFICATION AND CLASSIFICATION OF UNLABELED TRACKS, MS Computer Science, 26Sep25

McMurtray, Aran Pascal, A COMPARISON OF ON-THE-EDGE MACHINE LEARNING CLASSIFICATION METHODS FOR FLIGHT PLANNING, MS Computer Science, 26Sep25

Pham, Christopher de Leon, ENHANCING ROBUSTNESS OF VISION TRANSFORMERS WITH PADNET DEFENSE, MS Computer Science, 25Sep26

Maldonado Mundo, Francisco Daniel, AI-DRIVEN SPATIOTEMPORAL INTRUSION DETECTION ON TIME-EVOLVING 3D NETWORKS, MS Computer Science, 25Sep26

Ong, Justman, EVALUATING SELF-SUPERVISED ANOMALY DETECTION THROUGH NETWORK TRAFFIC METADATA ON OPERATIONAL ENTERPRISE NETWORKS, MS Artificial Intelligence, 25Sep27

DISSERTATION ADVISING

Jatho, Edgar Wilfred, FINDING AND FIXING FRAGILITY IN MACHINE LEARNING, PhD Computer Science, 16Jun23 (Supervisor)

Walsh, Timothy Cotnoir, OPEN-WORLD VIDEO STREAM FINGERPRINTING, PhD Computer Science, 18Jun25 (Supervisor-Chair)

Shaikh Akib Shahriyar, ROBUSTDF: DEVELOPING A PRACTICAL AND ROBUST DEFENSE FRAMEWORK FOR DEEPFAKE DETECTION, PhD Computer Science, Fall 2027

INVITED TALKS

Armon Barton. Towards Defending Deep Neural Networks Against Adversarial Examples, and Predicting Efficient and Anonymous Tor Circuits. Naval Postgraduate School, Monterey, CA, USA, March 4, 2019.

Armon Barton, Mohsen Imani, Jiang Ming, and Matthew Wright. Poster: PredicTor: Predicting Fast Circuits For A Faster User Experience in Tor. In the 38th IEEE Symposium on Security and Privacy (S&P’17) Poster Session. San Jose, CA, USA, May 22-24 2017.

PROFESSIONAL SERVICES

Reviewer for International Conference on Machine Learning Applied

Reviewer for IEEE Transactions on Network and Service Management

Reviewer for IEEE Transactions on Network Science and Engineering

Reviewer for Privacy Enhancing Technology Symposium

Reviewer for IEEE Computer Magazine

Reviewer for Mathematics

Reviewer for Symposium on Applied Computing