Artificial intelligence (AI) methods have been widely applied for accurate network intrusion detection (NID). However, the developers and users of the NID systems could not understand the systems' correct or incorrect decisions due to the complexity and black-box nature of the AI methods. This is a two-page poster paper that presents a new demo system that offers a number of counterfactual explanations visually for any data example. The visualization results were automatically generated: users just need to provide the index of a data example and do not edit anything on the graph. In the future, we will extend the detection task from binary classification to multi-class classification.