MF2-DMTD: A Formalism and Game-Based Reasoning Framework for Optimized Drone-Type Moving Target Defense

Moving-target-defense (MTD) fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations. However, the existing naive MTD studies were conducted focusing only on wired network mutations. And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations, such as hostility, mobility, and dependency. Therefore, to solve these conceptual limitations, this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle to adaptively secure the sustainability of drone operations. In addition, this study also specifies MF2-DMTD (model-checkingbased formal framework for drone-type MTD), a formal framework that adopts model-checking and zero-sum game, for attack-defense simulation and performance evaluation of drone-type MTD. Subsequently, by applying the proposed models, the optimization of deceptive defense performance of drone-type MTD for each mutation period also additionally achieves through mixed-integer quadratic constrained programming (MIQCP) and multiobjective optimization-based Pareto frontier. As a result, the optimal mutation cycles in drone-type MTD were derived as (65, 120, 85) for each control-mobility, telecommunication, and payload component configured inside the drone. And the optimal MTD cycles for each swarming cluster, ground control station (GCS), and zone service provider (ZSP) deployed outside the drone were also additionally calculated as (70, 60, 85), respectively. To the best of these authors' knowledge, this study is the first to calculate the deceptive efficiency and functional continuity of the MTD against drones and to normalize the trade-off according to a sensitivity analysis with the optimum.