**6. Conclusions**

A smart city facilitates the life of its citizens by providing better services than nonsmart cities. Due to the extensive presence of digital data, smart cities are also vulnerable to various types of attacks. Machine-learning-based cyber threat intelligence can secure smart city environments by monitoring attacks and analyzing data threats in order to take prevention measures. In this paper, we have proposed a hybrid deep learning model to classify threats. The proposed model uses a CNN and a QRNN to improve feature extraction, increases classification accuracy, and lower the FPR. We evaluated our model on the BoT-IoT and TON\_IoT datasets, and our results showed the effectiveness of our model in improving classification accuracy and lowering the FPR. In addition, the results showed that the QRNN model could improve classification time performance while providing high accuracy and lower FPR than LSTM. Thus, the proposed model for CTI for smart cities can accurately analyze and classify data in real time.

One of the limitations of this work is the authors' use of datasets. Due to the security and privacy of smart city citizens, it was difficult to evaluate the proposed model on real-time data. Additionally, for implementation, we evaluated the model as a centralized system. In future work, we can implement the proposed model in a distributed environment with parallel training to improve classification performance.

**Author Contributions:** Data curation, N.A.-T.; Formal analysis, N.A.-T.; Funding acquisition, N.A.S.; Investigation, N.A.S. and N.A.-T.; Project administration, N.A.S.; Resources, N.A.S.; Supervision, N.A.S.; Validation, N.A.-T.; Writing—original draft, N.A.-T. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to thank SAUDI ARAMCO Cybersecurity Chair, Imam Abdulrahman Bin Faisal University for funding this project.

**Acknowledgments:** The authors would like to thank Attaur-Rahman and Sujata Dash for their feedback on an earlier non-peer-reviewed version of the manuscript which was shared on the Arxiv repository.

**Conflicts of Interest:** The authors declare no conflict of interest.
