# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 5 Conclusions  
This work addresses the application and evaluation of anomaly detection models in the context of smart cities supported by IoT. Anomaly detection models, including Isolation Forest, RNNs, and VAEs, have demonstrated robust effectiveness in identifying anomalies in urban data. These models have highlighted their ability to detect unusual patterns in electrical consumption, manipulations in the electrical grid, and cyber threats, which supports their relevance in monitoring critical systems in smart cities.  
The results of this work suggest that anomaly detection models can be applied to various scenarios in smart cities. In addition to the electrical grid, these models can be helpful in areas such as traffic monitoring, waste management, and air quality surveillance, thus contributing to improving citizens' quality of life.  
This study has exhaustively evaluated the effectiveness of various anomaly detection models in the context of IoT in smart cities. The results support the usefulness of these models to accurately and timely identify a wide range of anomalies, from unusual fluctuations in electrical energy demand to voltage manipulation attempts and cyber-attacks. These findings support the applicability and relevance of anomaly detection models to the safety and efficiency of critical infrastructure in smart urban environments.  
This study has highlighted the effectiveness of anomaly detection models and contributed significantly to the literature on IoT in smart cities. Delving into applying these models in various urban scenarios and evaluating their performance has provided a deeper understanding of how machine-learning approaches can strengthen safety and efficiency in this context. Furthermore, the results presented here support the relevance of addressing anomaly detection as a critical component of cybersecurity in the essential infrastructure of smart cities.  
Early detection of anomalies in urban data can strengthen the security of smart cities. The proposed models have demonstrated their ability to identify tampering attempts, failures in electrical infrastructure, and cyber threats, allowing preventive measures to be taken and safeguarding the integrity of urban systems. This work adds to the existing literature on IoT in smart cities and anomaly detection. It is related to previous studies highlighting the importance of guaranteeing the security and privacy of data generated by IoT devices in urban environments.  
Future work can explore the application of anomaly detection models to various additional applications in smart cities, such as water quality monitoring, smart building management, and public transportation optimization. Smart cities generate data from multiple sources, such as IoT sensors, surveillance cameras, and geographic information systems. Future work can address integrating various data sources for even more robust and accurate anomaly detection.  
Conceptualization, W.V.-C.; methodology, A.J.-A.; software, J.G.; validation, A.J.-A.; formal analysis, W.V.-C.; investigation, A.J.-A.; data curation, W.V.-C. and J.G.; writing--original draft preparation, A.J.-A.; writing--review and editing, J.G.; visualization, A.J.-A.; supervision, W.V.-C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. The authors declare no conflict of interest.