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Abstract: With the exponential growth of internet traffic and the increasing complexity of networked systems, accurate traffic classification has become a crucial task for network management and security. Deep Learning (DL) techniques have shown promising results in various domains, including traffic classification. However, the effectiveness of DL models heavily relies on the selection of relevant features from raw network traffic data. In this paper, we propose a novel approach for traffic classification by integrating Deep Learning with Genetic Algorithm (GA) for feature selection. The proposed method aims to enhance the performance of traffic classification models by identifying and utilizing dominant features extracted from raw network traffic data. We demonstrate the efficacy of our approach through comprehensive experiments conducted on benchmark datasets, showcasing improved classification accuracy compared to existing methods.