Data-Driven Learning Techniques for Cyber-Physical-Situation Awareness in Defense Systems
Abstract
The objective of this project is to investigate and develop novel data-driven learning techniques for real-time and adaptive cyber-p"hysical situation awareness of adversarial activities, and feed information about the impact of those activities back to the securit"y analysts. This will significantly enhance awareness and safety in defense systems by providing fast decision making along with guarantees for mission completion and conditions for robustness. The guaranteed robustness of the proposed data-driven learning techniques will ensure fast prompt and/or automated decision making without reaching back to higher command levels. The technical approach" is to be inspired by interdisciplinary ideas from different fields, such as game theory, cognitive learning (specifically reinforce""ment learning), cyber-physical systems, and network security. The anticipated outcomes include transformative data-driven learning a""lgorithms to engage the DoD security analysts in the characterization, recognition, and mitigation of adversarial actions, with the"" support of rapid decision-making through prompt action, and recommendations/actions from ""automated assistants."" The contributions"" and impact on DoD goals will be the ability to train security analysts to engage in prompt and automated inputs, multiply engagemen""t capability, and enable mission completion of hetero-geneous cyber-physical systems (CPS) in hostile, noisy, and completely uncerta"in environments. We shall work with the DoD to transfer the proposed technology as we have successfully done in the past.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 20, 2018
- Source ID
- N000141812160
Entities
People
- Kyriakos G Vamvoudakis
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech