Composable Formal Models
for
High-Assurance Fault Tolerant Networks

Investigators


Objective

Current network systems are highly vulnerable to attacks. They are typically not capable of self-recovery, and even if they can recover they remain vulnerable to the same attacks in the future. A number of promising techniques and experimental systems are aimed at monitoring, diagnosing, and countering network attacks. The very ideas of self-monitoring, self-diagnosing, and self-repairing are intrinsically reflective; that is, they involve a system having the capacity to represent some aspects of itself and its environment and to use this to adapt its behavior to changing situations. Most existing systems exhibit some implicitly reflective features, but they lack systematic methods and techniques for taking advantage of the power of reflection. Furthermore, there is a lack of formal models for these systems. Having such formal models is key to gaining high assurance for this critical infrastructure. The project will develop a formal reflective framework for network recovery and reconstitution (R and R) and will use this framework to facilitate the use of formal models and analysis in the design of R and R systems in collaboration with other teams in the Fault Tolerant Networks (FTN) program. Besides providing higher assurance for such systems, we expect the proposed work to have the following additional benefits\:

Approach

The project will build on our expertise in formal modeling of reflective object-based distributed systems, and on our experience in applying formal methods to the design and analysis of Active Network systems. The aspects of the appoach include: An important aspect of the effort will involve case studies in collaboration with other teams in the FTN program. These collaborations will give us a rich conceptual testbed with which to develop the proposed formal reflective framework, models, tools and analysis techniques, and will enable higher assurance in the design of FTN systems by other research teams.