Internet of things (IoT) aims at connecting a large number of devices for supporting “Connectivity to All” in the 5G networks. With connections between the majority of computing devices, capturing a single entity can expose the perimeter of the entire network. Remote diagnosis of the IoT devices can help in identification of such loopholes. However, if an intruder is already present in the network, it can falsify the diagnostic procedures and can cause serious threats to the network. Thus, an efficient strategy is required which can provide remote diagnosis along with the secure validation of IoT devices. In this paper, fuzzy logic is used to resolve the safety decisions and remote diagnosis of IoT devices in 5G networks. The proposed solution uses a two-pass methodology to generate inference rules at the central as well as the local inference engine. The proposed approach evaluates the network in two phases. The first phase emphasizes on the remote diagnosis and the second phase emphasizes on the remote validation. On the basis of these phases, a remote assessment protocol is also proposed which helps in remote validations with lower overheads and ease of deployment.