Game Theory and Machine Learning for Cyber Deception

This research opportunity is to develop novel methods for cyber deception. The candidate will be responsible for leading the design, development, publication, and prototyping of novel adaptive, proactive, and reactive cyber deception systems to ensure authentic, accurate, secure, and reliable communication networks. The developed models should focus on the generation, deployment, design, and reconfiguration of decoy devices such as honeypots, honeynets, honey-tokens, etc. Algorithms should apply to complex adversarial decision-making based on game theory, reinforcement learning, and utilizing state-of-the-art AI algorithms to dynamically adapt to, and learn from human or agent actions and contextual situations and adversarial environments. The Army Research Laboratory Research Associateship Program (ARL-RAP) is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. This fellowship opportunity is within the Network Cyber & Computational Sciences (NCCS) division, which focuses on enabling and ensuring secure resilient communication networks for distributed analytics in Multi-Domain Operations. The ideal candidate will be interested in developing novel AI algorithms and decision theories based on cross-disciplinary approaches. This role requires experience in Game Theory, Multi-agent Reinforcement Learning, and Machine Learning. Scientists and Engineers at the DEVCOM Army Research Laboratory help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs.