学术文献库

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.