Distributed denial-of-service (DDoS) assaults represent a substantial menace in contemporary security of networks, demanding effective detection mechanisms to mitigate their escalating impact. Despite notable progress in related research, the diverse attack modes and fluctuating scale of malicious traffic continue to challenge the development of detection methods with optimal accuracy. This paper addresses this gap by proposing a comprehensive DDoS attack detection approach leveraging deep learning methodologies. The NSL-KDD Dataset serves as the experimental foundation for training, testing, and validating deep learning algorithms. The proposed method integrates the Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithm, enhancing model performance, mitigating overfitting, and reducing computational complexity. The classifier comprises Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) components. CNN excels at object detection and localization within images or videos, while the GRU provides a dynamic mechanism for selectively updating the network’s hidden state, effectively managing flow information. The experimental results demonstrate the efficacy of the proposed approach in achieving improved detection accuracy and robust performance against DDoS attacks.