This paper addresses the challenge of detecting and mitigating Distributed Denial-of-Service (DDoS) attacks in cloud computing environments. Traditional approaches to DDoS detection have limitations in terms of accuracy and scalability, leading to the emergence of machine learning techniques. However, there are still challenges in achieving accurate and efficient detection, particularly in dynamic and complex cloud environments. This paper proposes an ensemble optimized Deep Neural Network (DNN) for detecting DDoS attacks in the cloud, leveraging the strengths of individual models and mitigating their weaknesses. The paper investigates the impact of hyperparameter tuning on the performance of the ensemble optimizer DNN. The proposed approach aims to improve the accuracy and efficiency of DDoS detection in the cloud, highlighting the main differences between this approach and existing ones. The paper concludes with a discussion of the findings and future research directions.