The　rapid　expansion　of　Internet　users　results　in　an　immense　influx　of　network　traffic　within　extensive　cloud　data　centers.　Accurate　and　instantaneous　identification　and　forecasting　of　network　traffic　aid　system　managers　in　efficiently　distributing　resources,　assessing　network　performance　based　on　specific　service　demands　and　scrutinizing　the　health　of　network　status.　However,　sources　and　distributions　of　traffic　are　different,　which　makes　accurate　warnings　of　cyberattack　traffic　difficult.　Recently,　emerging　neural　networks　have　demonstrated　their　efficacy　in　forecasting　time　series　data　of　network　cyberattacks.　The　time　series　has　temporal　and　spatial　features,　which　can　be　efficiently　captured　with　Informer　and　convolutional　neural　networks.　To　realize　high-performance　spatiotemporal　detection　of　cyberattacks,　this　work　for　the　first　time　designs　a　hybrid　and　spatiotemporal　prediction　framework,　which　integrates　convolutional　neural　networks,　Informer,　and　a　Softmax　classifier　to　realize　high　classification　accuracy　of　normal　and　abnormal　cyberattacks.　Real-life　data　are　adopted　to　evaluate　the　proposed　method,　which　yields　significant　improvement　in　classification　accuracy　over　typical　benchmark　classification　models.　IEEE