Optimizing Programmable Logic Design Security Strategies
A wide variety of design security strategies have been developed for programmable logic devices, but less work has been done to determine which are optimal for any given design and any given security goal. To address this, we consider not only metrics related to the performance of the design security practice, but also the likely action of an adversary given their goals. We concern ourselves principally with adversaries attempting to make use of hardware Trojans, although we also show that our work can be generalized to adversaries and defenders using any of a variety of microelectronics exploitation and defense strategies. Trojans are inserted by an adversary in order to accomplish an end. This goal must be considered and quantified in order to predict the adversary's likely action. Our work here builds upon a security economic approach that models the adversary and defender motives and goals in the context of empirically derived countermeasure efficacy metrics. The approach supports formation of a two-player strategic game to determine optimal strategy selection for both adversary and defender. A game may be played in a variety of contexts, including consideration of the entire design lifecycle or only a step in product development. As a demonstration of the practicality of this approach, we present an experiment that derives efficacy metrics from a set of countermeasures (defender strategies) when tested against a taxonomy of Trojans (adversary strategies). We further present a software framework, GameRunner, that automates not only the solution to the game but also enables mathematical and graphical exploration of "what if" scenarios in the context of the game. GameRunner can also issue "prescriptions," sets of commands that allow the defender to automate the application of the optimal defender strategy to their circuit of concern. We also present how this work can be extended to adjacent security domains. Finally, we include a discussion of future work to include additional software, a more advanced experimental framework, and the application of irrationality models to account for players who make subrational decisions.