<p>The Internet of Bio-Nano Things (IoBNT) is one of the main emerging technologies for enabling the development of sixth generation (6G) healthcare systems through the use of molecular communications (MC) for the data exchange into the in-body network. However, the secure transmission of data in these complex biological environments and against the threats of eavesdropping and dynamic physiological conditions remains a key problem. In this paper, the authors present the physical cyber security capacity (PCSC) framework, i.e., the maximum rate possible for secure and reliable data transfer in MC-based IoBNT systems in realistic biological environments defined by their physical parameter sets. An analytical framework is developed including theoretical models which combine diffusion and advection transmission mechanisms and account for physiological factors including blood velocity, blood pressure, and vascular geometries. Additionally, a Q-learning-based reinforcement learning (RL) framework is developed to adaptively optimize transmission parameters such as the time slot duration and the probability of molecule release while accounting for time-varying conditions. Results show that advection increases the security capacity of MC transmissions, as well as allowing for better security performance within a learning-based approach by increasing the security performance of the proposed solution by 64.9% of baseline solutions. In addition, this work integrates biologically, realistic channel models with adaptive artificial intelligence optimization to enable the use of secure and efficient IoBNT applications like targeted drug delivery and real-time monitoring of health.</p>