## 5 Conclusions  
The heterogeneity and interoperability requirements must be met when designing a smart city application. These stringent requirements must be met by all smart city components, including devices, network equipment, vendor-proprietary software, communication technologies and protocols, and a variety of other smart services and smart city applications. SDN has emerged as a possible resilient future internet architecture in recent years. Numerous recent studies have shed light on how SDN can be used to improve the resilience and security of communication networks in smart cities. This work conducted a comprehensive and in-depth study to explain the essentials of SDN from the resilience perspective, followed by a proposal of a secure and intelligent framework. XGBoost achieved an accuracy of 99.99%, a precision of 97%, a recall of 99%, an F1 score of 98%, and an FPR of 0.05 using binary classification. In multiclass classification, the average accuracy was 99.29%, precision 97.7%, recall 96.69%, and F1 score 97.51%. In our future work, we will explore and try the DDoS attack on high-performance targets. This project was funded by the Deanship of Scientific Research, University of Bisha, Bisha, Kingdom of Saudi Arabia. The experiment has been performed on the open dataset, which is freely available at IEEE Dataport: [PERSON], 16 October 2019, \"The Bot-IoT dataset\", IEEE Dataport, [[https://dx.doi.org/10.21227/r7v2-v988](https://dx.doi.org/10.21227/r7v2-v988)]([https://dx.doi.org/10.21227/r7v2-v988](https://dx.doi.org/10.21227/r7v2-v988)), accessed on 25 April 2023. The authors extend their appreciation to the Deanship of Scientific Research, University of Bisha, Saudi Arabia for funding this research work through the Promising Initiative Project under Grant Number (UB-Promising-08-1442). The authors declare no conflict of interest between the authors or institutions. Figure 10: Comparative analysis of accuracy with existing work [8]. * (17) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Design, deployment and use of HTTP-based botnet (HBB) testbed. In Proceedings of the 16 th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, Republic of Korea, 16-19 February 2014; pp. 1265-1269. * (18) [PERSON]; [PERSON]; [PERSON]; [PERSON] Using machine learning techniques to identify botnet traffic. In Proceedings of the 31 st IEEE Conference on Local Computer Networks, Tampa, FL, USA, 14-16 November 2006. * (19) [PERSON]; [PERSON]; [PERSON]; [PERSON] A framework for generating realistic traffic for distributed denial-of-service attacks and ash events. _Comput. Secur._**2014**, _40_, 95-107. [CrossRef]
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