Accurately　identifying　network　intrusion　cannot　only　help　individuals　and　enterprises　better　deal　with　network　security　problems,　but　also　maintain　the　Internet　environment.　Currently,　classification　methods　with　autoencoders　for　feature　learning　have　been　proved　to　be　suitable　for　the　network　intrusion　detection.　This　work　proposes　a　new　hybrid　classification　method　named　SABD　for　network　intrusion　detection.　SABD　integrates　Stacked　sparse　contractive　autoencoders,　Attention-based　Bidirectional　long-term　and　short-term　memory　(LSTM),　and　Decision　fusion.　SABD　integrates　the　feature　extraction　of　stacked　sparse　contractive　autoencoders　with　the　classification　ability　of　attention-based　bidirectional　LSTM.　Specifically,　stacked　sparse　contractive　autoencoders　are　used　for　extracting　features,　which　are　sent　to　the　attention-based　bidirectional　LSTM　for　the　classification.　Finally,　the　decision　fusion　algorithm　is　adopted　to　integrate　classification　results　of　multiple　classifiers　and　yield　the　final　results.　Experimental　results　based　on　real-life　UNSW-NB15　data　demonstrate　that　the　proposed　SABD　outperforms　its　state-of-the-art　peers　in　terms　of　classification　accuracy.　©　2022　IEEE.