Real-Time DDoS flood Attack Monitoring and Detection (RT-AMD) Model for Cloud Computing

Abstract

In recent years, the advent of cloud computing has transformed the field of computing and information technology. It enabled customers to rent virtual instances and take advantage of various services on-demand with the lowest costs. Despite the advantages offered by cloud computing, it faces several threats; an example is DDoS attack which is considered one of the most serious ones. This paper proposes a real-time monitoring and detection of DDoS attacks on the cloud using machine learning approach. Naïve Bayes, K-Nearest Neighbor, and Random Forest machine learning classifiers have been selected to build predictive models. This model will be evaluated on the cloud for its accuracy and efficiency.