Unmanned Aerial Vehicles (UAVs) and their communication networks have emerged as versatile platforms with the potential to revolutionize various industries and applications. However, these networks are vulnerable to security threats that can compromise operations and data integrity. One such threat is the network inference attack, a technique used to indirectly deduce sensitive flow information by analyzing readily available link metrics within a UAV network. Traditional reactive defenses against inference attacks in UAV networks are primarily based on detecting an attack once it has already started. This approach leads to a critical period during which the initial stages of the attack could remain undetected, putting sensitive information at risk of exposure. In this chapter, we observe that the inference error is closely related to the likelihood of discrepancies arising between the flow patterns observed by attackers and the genuine template operational within the network. Motivated by this observation, we introduce a proactive defense strategy designed to counter inference attacks, called MaxPro. This approach involves using a dynamic routing protocol for UAV networks that continuously alters the routing pattern. The goal is to maximize the probability that the routing pattern observed by attackers will not match the actual routing pattern used within the network, thereby increasing the inference error and leading to falsified information obtained by attackers. We conducted extensive theoretical and empirical analyses to demonstrate that our proposed method, MaxPro, is capable of achieving an inference error that is proportional to the number of UAVs within the network.