Survival analysis encompasses the contemplation of the time between a static initial point (such as the detection of instability in interconnected devices) and a final stage (like failure/malfunction). The key characteristic that differentiates this collected data from other categories is that the occurrence does not necessarily happen in all devices by the expiration of the observation, leaving the full survival rates of these machines unidentified. For example, in study that computes the length of survival time after the initial detection, it is frequent for a percentage of devices to remain active and stable until the end of the study and inspecting period. Consequently, we can only determine a lower bound on their operational lifespan. Hence, special techniques are required for these types of data collection. In this article, we explain a mechanism for analyzing survival time of individual device including the denotation of risk ratio. We also consider multiple covariate models and, in specific, how to associate the impact of factors that predict survival to the robustness of an enterprise private network. The results indicate that in order to achieve high security for the entire enterprise private network, we need to focus on devices with a high number of failures.