Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

Abstract

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.