Security-Aware Synthesis Using Delayed-Action Games

Abstract

Stochastic multiplayer games (SMGs) have gained attention in the field of strategy synthesis for multi-agent reactive systems. However, standard SMGs are limited to modeling systems where all agents have full knowledge of the state of the game. In this paper, we introduce delayed-action games (DAGs) formalism that simulates hiddeninformation games (HIGs) as SMGs, where hidden information is captured by delaying a player's actions. The elimination of private variables enables the usage of SMG off-the-shelf model checkers to implement HIGs. Furthermore, we demonstrate how a DAG can be decomposed into subgames that can be independently explored, utilizing parallel computation to reduce the model checking time, while alleviating the state space explosion problem that SMGs are notorious for. In addition, we propose a DAG-based framework for strategy synthesis and analysis. Finally, we demonstrate applicability of the DAG-based synthesis framework on a case study of a human-on-the-loop unmanned-aerial vehicle system under stealthy attacks, where the proposed framework is used to formally model, analyze and synthesize security-aware strategies for the system.

Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning

“…This work is motivated by the reported susceptibility of autonomous systems to attacks [1], [3], [26]. Several methods are proposed to deal with attacks on system sensing, both for low-level control (e.g., [27]- [29]), and path planning (e.g., [30], [31]). However, to the best of our knowledge, no methods exist to design controller strategies for uncertain environments, such that the derived controllers are maximally resilient to actuation attacks -this is the focus of this work.…”