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.　(C)　2021　Elsevier　B.V.　All　rights　reserved.