  The open radio access network (O-RAN) architecture's native virtualization
and embedded intelligence facilitate RAN slicing and enable comprehensive
end-to-end services in post-5G networks. However, any vulnerabilities could
harm security. Therefore, artificial intelligence (AI) and machine learning
(ML) security threats can even threaten O-RAN benefits. This paper proposes a
novel approach to estimating the optimal number of predefined VNFs for each
slice while addressing secure AI/ML methods for dynamic service admission
control and power minimization in the O-RAN architecture. We solve this problem
on two-time scales using mathematical methods for determining the predefined
number of VNFs on a large time scale and the proximal policy optimization
(PPO), a Deep Reinforcement Learning algorithm, for solving dynamic service
admission control and power minimization for different slices on a small-time
scale. To secure the ML system for O-RAN, we implement a moving target defense
(MTD) strategy to prevent poisoning attacks by adding uncertainty to the
system. Our experimental results show that the proposed PPO-based service
admission control approach achieves an admission rate above 80\% and that the
MTD strategy effectively strengthens the robustness of the PPO method against
adversarial attacks.
