Analysis, Design, and Operation of Resilient Networks Against Localized, Strategic, and Dynamic Adversaries
Abstract
Dynamic networks are critical in multiple operating theaters of interest to the U.S. Air Force. These distributed systems have achieved great levels of autonomy, which guarantee success under nominal operating conditions in different time critical missions. Unfortunately, despite recent efforts to characterize attack vectors and countermeasures, research studies and real world incidents have demonstrated how capable adversaries can severely disrupt the operation of dynamic networks, thereby questioning their resiliency to operate in uncertain and adversarial environments. Novel theories and tools are critically needed to guarantee resiliency of dynamic networks, where both the network protocols and the attack strategy adapt over time as in an intricate game. In this project we characterize vulnerabilities of dynamic networks to attackers with different capabilities, knowledge, and objectives, and design rigorous methods to protect dynamic networks and adapt them to ensure resiliency to attacks. In particular, the PIs will (i) characterize fundamental resiliency limitations of networks against partially informed attackers, (ii) design topology adaptation algorithms to enhance resiliency of dynamic networks, and (iii) design strategies for the network operator to detect and adapt to adversaries. This project will undertake a rigorous approach based on dynamics, control, graph, and game theories, and will validate the findings through simulations and experiments with state of the art facilities. In addition to allowing the deployment of reliable networks and distributed systems in hostile environments, the theories and methods developed in this project will have far reaching implications and applicability across science and engineering, including medical, automotive, and energy sectors, as already demonstrated by the PIs work.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 14, 2022
- Source ID
- FA95501910235
Entities
People
- Fabio Pasqualetti
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of California Regents