Predictive Security Metrics - Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. LogLoss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.