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Advanced Persistent Threats (APTs) have recently emerged as a significantsecurity challenge for a cyber-physical system due to their stealthy, dynamicand adaptive nature. Proactive dynamic defenses provide a strategic andholistic security mechanism to increase the costs of attacks and mitigate therisks. This work proposes a dynamic game framework to model a long-terminteraction between a stealthy attacker and a proactive defender. The stealthyand deceptive behaviors are captured by the multi-stage game of incompleteinformation, where each player has his own private information unknown to theother. Both players act strategically according to their beliefs which areformed by the multi-stage observation and learning. The perfect Bayesian Nashequilibrium provides a useful prediction of both players' policies because noplayers benefit from unilateral deviations from the equilibrium. We propose aniterative algorithm to compute the perfect Bayesian Nash equilibrium and usethe Tennessee Eastman process as a benchmark case study. Our numericalexperiment corroborates the analytical results and provides further insightsinto the design of proactive defense-in-depth strategies.

A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems