With the rapid expansion of global networks and the proliferation of IoT devices, the complexity and scale of traffic have grown exponentially. This surge in connectivity demands larger, faster, and more serviceable architectures, like Software Defined Networks (SDNs). Motivated by various interests, malicious agents seek to compromise services within the network with different attacks. Intrusion Detection Systems (IDSs) are solutions often implemented in SDN using Deep Learning algorithms. These methods are more challenging to explain as they grow in complexity and become less trustworthy for handling sensitive issues like cyber security. This work uses SHapley Additive exPlanations (SHAP) to explain a consolidated IDS that combines Gated Recurrent Units (GRU) with Generative Adversarial Network’s discriminator. We conducted a feature selection based on the SHAP explanation and used its insights to better tune the time series’s window size hyperparameter. The optimized model performed similarly to the original, with a margin for improvement upon further hyperparameter tuning. It was also more stable in the training phase and faster to execute. This new version of the model was also explained by SHAP and presented a more consistent behavior.