In light of the achievements of deep learning in computer vision, natural language processing, and audio processing, recent research endeavors have been made to introduce novel deep learning models for tabular data. However, recent evaluations on small and medium-sized tabular datasets have shown that tree-based models still deliver better results than deep learning. One compelling rationale for this performance superiority lies in the non-rotational invariance property of tree-based models. However, in the context of intrusion detection with relatively large, the relationship between rotational invariance and the performance of these models remains unexplored. Therefore, this paper attempts to analyze the intrusion detection capabilities of tree-based and deep-learning models under rotations, shedding light on the relationship between the rotational invariance property and their detection performance. From our evaluation results, we draw three findings: (1) the effect of rotations on the models is different across datasets; (2) deep learning models are indeed less influenced by rotations; (3) deep learning models perform on par with tree-based models on intrusion datasets.