Recent advances in artificial intelligence have prompted the use of machine learning methods in network security. In this paper, we address the issue of imbalanced data that is often present in network security datasets used in machine learning. We propose an oversampling method based on the Gamma distribution to balance the data prior to training. The results on several imbalanced datasets show the potential of the proposed method as a viable tool to build intrusion detection systems based on artificial intelligence. The accompanying code for the study is available on Github .