Strengthening Open Radio Access Networks: Advancing Safeguards Through ZTA and Deep Learning.

Open Radio Access Networks (O-RAN) are gaining momentum because of their ability to provide greater vendor flexibility, cost-effectiveness, and scalability, making it an attractive choice for network operators. However, securing O-RAN has become an essential concern due to the inherent vulnerabilities and risks associated with their open nature. Zero trust architecture (ZTA) can help address the security issues associated with O-RAN. ZTA is a security model that assumes that all devices, users, and applications are potentially hostile and cannot be trusted until verified. In addition to ZTA, the integration of deep learning techniques can allow for the detection and prevention of sophisticated cyber threats in real-time. In this work, we propose a novel approach to securing O-RAN using ZTA and Deep Sarsa reinforcement learning algorithms. First, we developed a ZTA model using an open-source approach that can be used as the base architecture for our study. Then, we present our proposed approach, which uses Deep Sarsa to learn the optimal policy for enforcing access control rules on the network resources based on user authentication data from ZTA. Finally, we evaluate our model using real data sets and show that it performs better than other approaches in terms of accuracy, F1 score, and precision. Our results demonstrate that combining ZTA with reinforcement learning is a promising way to help secure O-RAN while still providing flexible access control policies for operators.