Cyber Threat Intelligence for Secure Smart City
This work addresses cyber threat classification for smart city security, but it appears incremental as it builds on existing deep learning methods without introducing a major breakthrough.
The paper tackles the problem of securing smart city networks from cyber threats by proposing a hybrid deep learning model combining CNN and QRNN for real-time threat classification, achieving improved performance over state-of-the-art models.
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.