Research involves developing ways of detecting new kinds of cyberattacks using honeypots (decoy digital systems), especially those simulating cyber-physical systems. We are collecting network-traffic data using various kinds of deception and are trying to find patterns in it using machine-learning techniques. We are particularly interesed in methods to subvert machine learning with manipulated data ("adversarial machine learning"). Related work focuses on disk-drive forensics.
N. C. Rowe, Identifying forensically uninteresting files in a large corpus. EAI Endorsed Transactions on Security and Safety, Vol. 16, No. 7, article e2, 2016.
N. C. Rowe, Honeypot deception tactics. Chapter 3 in E. Al-Shaer, J. Wei, K. Hamlen, and C. Wang (Eds.), Autonomous Cyber Deception: Reasoning, Adaptive Planning, and Evaluation of HoneyThings, Springer, Chaum, Switzerland, 2018, pp. 35-45.
J. S. Dean and N. C. Rowe, Utility of user roles in comparing network flow behaviors. Proc. Intl. Conf. on Computational Science and Computational Intelligence, December 2018, Las Vegas, NV, USA.
Find and choose an agency to see details and to explore individual opportunities.