Accurately　identifying　network　intrusion　cannot　only　help　individuals　and　enterprises　better　deal　with　network　security　problems,　but　also　maintain　the　Internet　environment.　This　work　proposes　a　new　hybrid　classification　method　named　SABD　for　network　intrusion　detection.　SABD　integrates　Stacked　sparse　contractive　autoencoders　(SSCA),　Attention-based　Bidirectional　long-term　and　short-term　memory　(LSTM),　and　Decision　fusion.　Specifically,　SSCA　is　used　for　extracting　features,　which　are　sent　to　the　attention-based　bidirectional　LSTM　for　the　classification.　Besides,　an　improved　optimization　algorithm　named　genetic　simulated-annealing-based　particle　swarm　optimization　is　designed　to　optimize　hyperparameters　of　SSCA.　Finally,　the　decision　fusion　algorithm　is　adopted　to　integrate　classification　results　of　multiple　classifiers　and　yield　the　final　results.　Based　on　experimental　results　from　four　different　types　of　data　sets,　the　proposed　SABD　outperforms　its　most　advanced　peers　in　classification　accuracy.