<p>The combination of the Internet of Things (IoT) with Software Defined Networking (SDN) technologies poses issues in terms of security and scalability. There are serious security vulnerabilities associated with traditional SDN systems since centralized controllers control them and are subject to manipulation by adversaries. In response to these issues, this paper suggests a unique method for automatically establishing security protocols in SDN settings by combining a Dynamic Trust Management Framework with the state-of-the-art reinforcement learning system Soft Actor-Critic (SAC). Our approach adjusts policy priorities dynamically depending on the reliability of network entities in real-time, improving security measures by using SAC’s capacity to learn optimum rules from data and adapt to changing network dynamics. To ensure authenticity and define user-specific attributes for access control, authentication methods are enforced throughout the registration process for both users and applications. With the use of SAC, security rules are created with permission activities, factual information, and temporal considerations in mind, strengthening network defenses against possible attacks. Moreover, policy conflicts are reduced by validation and storage in a centralized database, which streamlines administrative work. Our suggested model outperforms current approaches in performance assessments that are carried out via extensive metrics analysis and simulation using the iFog Sim tool. The outcomes of the simulation demonstrate how well our method works to improve the security and scalability of SDN, which is a major step forward for the security of networks enabled by the Internet of Things.</p>