The intrinsic features of Internet networks lead to imbalanced class distributions when datasets are conformed, phenomena called Class Imbalance and that is attaching an increasing attention in many research fields. In spite of performance losses due to Class Imbalance, this issue has not been thoroughly studied in Network Traffic Classification and some previous works are limited to few solutions and/or assumed misleading methodological approaches. In this study, we propose a method for generating network attack traffic to address data imbalance problems in training datasets. For this purpose, traffic data was analyzed based on deep packet inspection and features were extracted based on common traffic characteristics. Similar malicious traffic was generated for classes with low data counts using Wasserstein generative adversarial networks (WGAN) with a gradient penalty algorithm. The experiment demonstrated that the accuracy of each dataset was improved by approximately 5% and the false detection rate was reduced by approximately 8%. This study has demonstrated that enhanced learning and classification can be achieved by solving the problem of degraded performance caused by data imbalance in datasets used in deep learning based intrusion detection systems.