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Detection and prevention of distributed denial of service (DDoS) attacks are considered to be a keystone of network security. Though a good number of potential solutions have been provided for the detection of attacks, but due to frequent change in attack vectors, a competent technique is essential for combating these new attacks. In this paper, we propose a hybrid method which uses deep neural network (DNN) model for distinguishing DDoS attacks in cloud environment using ant colony optimization (ACO) for learning prime or important hyperparameters for effective classification of DNN. The use of optimal parameters in DNN makes it more accurate for detection of attacks. The proposed approach is validated by comparing its performance w.r.t. parameters detection accuracy, detection rate with the results of three other recent methods based on machine learning. Experiments have been conducted on …