Network　traffic　anomaly　detection　involves　the　rapid　identification　of　intrusions　within　a　network　through　the　detection,　analysis,　and　classification　of　network　traffic　data.　The　variety　of　cyberattacks　encompasses　diverse　attack　principles.　Employing　an　indiscriminate　feature　selection　strategy　may　lead　to　the　neglect　of　key　features　highly　correlated　with　specific　attack　types.　This　oversight　could　diminish　the　recognition　rate　for　that　category,　thereby　impacting　the　overall　performance　of　the　detection　model.　To　address　this　issue,　this　paper　proposes　a　network　traffic　anomaly　detection　model　based　on　the　fusion　of　attack-dimensional　features.　Firstly,　construct　binary　classification　datasets　independently　for　each　attack　class　and　perform　individual　feature　selection　to　extract　positively　correlated　features　for　each　class.　The　features　are　then　fused　by　employing　a　combination　methods.　Subsequently,　based　on　the　fused　sub-datasets,　base　classifiers　are　trained.　Finally,　an　ensemble　learning　approach　is　introduced　to　integrate　the　predictions　of　individual　classifiers,　enhancing　the　robustness　of　the　model.　The　proposed　approach,　validated　on　NSL-KDD　and　UNSW-NB15　benchmark　datasets,　outperforms　the　latest　methods　in　the　field　by　achieving　a　2%　and　7%　increase　in　precision　on　weighted　averages.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2025.