Nowadays,　rapid　development　of　Internet　has　brought　a　sharp　increase　in　traffic　data.　Abnormal　traffic　haS　serious　impact　on　network　security.　Traffic　anomaly　detection　can　be　achieved　by　extracting　characteristics　of　network　traffic　to　detect　anomalous　intrusions,　and　therefore,　anomaly　detection　algorithms　are　of　great　significance　to　maintenance　of　network　security.　This　work　proposes　a　hybrid　spatio-temporal　neural　network　with　attention　named　CTGA　to　effectively　identify　anomalous　traffic.　CTGA　combines　a　Convolutional　neural　network　(CNN),　a　Temporal　convolutional　network　(TCN),　a　bidirectional　Gated　recurrent　unit　network　(BiGRU),　and　a　self-Attention　mechanism.　It　automatically　extracts　temporal　and　spatial　features　of　sequences　from　raw　data　by　sliding　window　preprocessing　followed　by　CNN,　TCN,　BiGRU,　and　the　self-attention　mechanism　to　detect　anomalous　data.　CNN　is　used　to　extract　spatial　features　of　time　sequences　and　reduce　the　loss　of　spatial　information.　In　the　sequence,　TCN　obtains　short-term　features.　Long-term　dependencies　in　the　data　are　captured　by　BiGRU,　and　the　self-attention　mechanism　obtains　important　information　in　the　sequence.　Finally,　experiments　with　the　real-life　Yahoo　S5　dataset　prove　that　CTGA　outperforms　other　approaches　substantially.　©　2023　IEEE.