Robustness-Aware Machine Learning for Network Intrusion Detection under Feature and Distribution Variations Using UNSW-NB15
Network intrusion-detection models are commonly evaluated with a fixed training and testing configuration. Such an evaluation does not fully show how a classifier behaves when feature values or class distributions change. This paper evaluates the robustness of a Random Forest model using the UNSW-NB15 dataset. The expe...