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DDoS attacks are an immense threat to online services, and numerous studies have been done to detect and defend against them. DDoS attacks, however, are becoming more sophisticated and launched with different purposes, making the detection and instant defense as important as analyzing the behavior of the attack during and after it takes place. Studying and modeling the Spatio-temporal evolvement of DDoS attacks is essential to predict, assess, and combat the problem, since recent studies have shown the emergence of wider and more powerful adversaries. This work aims to model seven Spatio-temporal behavioral characteristics of DDoS attacks, including the attack magnitude, the adversaries’ botnet information, and the attack’s source locality down to the organization. We leverage four state-of-the-art deep learning methods to construct an ensemble of models to capture and predict behavioral patterns of the attack. The proposed ensemble operates in two frequencies, hourly and daily, to actively model and predict the attack behavior and evolvement, and oversee the effect of implementing a defense mechanism.