MODEL-BASED SECURITY ANALYSIS OF FPGA DESIGNS THROUGH REINFORCEMENT LEARNING

Abstract

Finding potential security weaknesses in any complex IT system is an important and often challenging task best started in the early stages of the development process. We present a method that transforms this task for FPGA designs into a reinforcement learning (RL) problem. This paper introduces a method to generate a Markov Decision Process based RL model from a formal, high-level system description (formulated in the domain-specific language) of the system under review and different, quantified assumptions about the system’s security. Probabilistic transitions and the reward function can be used to model the varying resilience of different elements against attacks and the capabilities of an attacker. This information is then used to determine a plausible data exfiltration strategy. An example with multiple scenarios illustrates the workflow. A discussion of supplementary techniques like hierarchical learning and deep neural networks concludes this paper.