Distributed Denial-of-Service (DDoS) attacks are a security challenge for the Software-Defined Network (SDN). Effective traffic is critical for network durability and routine, necessitating intelligent and adaptive mechanisms. This paper presents a novel hybrid approach integrating Ant Colony Optimization (ACO) with Attention Convolutional Neural Network (CNN) to enhance cyber-attack. ACO optimizes Attention CNN parameters to improve learning efficiency and convergence, enabling dynamic adaptation to real-time network conditions. By using the global search capability of ACO and the predictive strength of Attention CNNs, the proposed method achieves better network traffic compared to other techniques. Using python software the ACO-Attention CNN significantly increase precision recall accuracy and ROC curve for cyber-attack. The proposed framework contributes to the development of intelligent, network traffic for DDoS attack, ensuring sustainable and resilient network communication.