With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets. The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively. The results show that ONTDAS can effectively enhance the traffic classifiers’ performance and significantly improve their generalizability on unknown malicious samples. The system can also alleviate dataset imbalance. Moreover, the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.