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.
Additional Benefits
Relocation
Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.
Health insurance
A group health insurance program is available to awardees and their qualifying dependents in the United States.