A robust network intrusion detection system (NIDS) plays an important role in cyberspace security for protecting confidential systems from potential threats. In real world network, there exists complex correlations among the various types of network traffic information, which may be respectively attributed to different abnormal behaviors and should be make full utilized in NIDS. Regarding complex network traffic information, traditional learning based abnormal behavior detection methods can hardly meet the requirements of the real world network environment. Existing methods have not taken into account the impact of various modalities of data, and the mutual support among different data features. To address the concerns, this paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively. In order to accurately explore the connotation of traffic information, multiple basic feature datasets are established considering different aspects of traffic information such as time, space, and load. Then, considering the association and correlation among the basic feature datasets, multiple comprehensive feature datasets are set up to meet the requirements of real world abnormal behavior detection. In specific, stacking ensemble learning is conducted on multiple comprehensive feature datasets, and thus an effective multi-dimensional global anomaly detection model is accomplished. The experimental results on the dataset KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017 have shown that MFFSEM significantly outperforms the basic and meta classifiers adopted in our method. Furthermore, its detection performance is superior to other well-known ensemble approaches. •The proposed approach based on multi-dimensional feature fusion and stacking ensemble learning.•We propose a multi-dimensional data partition and feature construction strategy.•A permutation and combination strategy is proposed to provide mutual support among different data features.•The proposed approach could lead to a more robust NIDS.