BG-FlipIn: A Bayesian Game Framework for FlipIt-Insider Models in Advanced Persistent Threats

In this paper, we study advanced persistent threats (APT) with an insider who has different pReferences. To address the uncertainty of the insider’s preference, we propose BG-FlipIn, a three-player Bayesian game framework for FlipIt-insider models that investigate malicious, inadvertent, and corrupt insiders. We derive a closed-form expression for the Bayesian Nash Equilibrium and obtain the Nash Equilibria for three deterministic edge cases. On this basis, we further discover several phenomena in APT related to the defender’s move rate and cost, as well as the insider’s preferences. We then provide decision-making guidance for the defender under different parametric conditions. Three applications validate that BG-FlipIn enables the defender to make consistent decisions without detecting the insider’s specific preference or frequently adjusting its strategy.