  Smart city improved the quality of life for the citizens by implementing
information communication technology (ICT) such as the internet of things
(IoT). Nevertheless, the smart city is a critical environment that needs to
secure it is network and data from intrusions and attacks. This work proposes a
hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve
threats classification performance based on convolutional neural network (CNN)
and quasi-recurrent neural network (QRNN). We use QRNN to provide a real-time
threat classification model. The evaluation results of the proposed model
compared to the state-of-the-art models show that the proposed model
outperformed the other models. Therefore, it will help in classifying the smart
city threats in a reasonable time.
