# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Security/Cybersecurity (4 Papers)
Smart city applications utilizing IoT devices face security challenges such as zero-day attacks, resource constraints, and limited security functionalities. The proposed approach is a machine learning-driven method for detecting attacks and anomalies specifically tailored for IoT-enabled smart cities, with an emphasis on distributed fog networks. Ensemble methods combining models from different sources are explored for enhanced performance compared to single classifiers. The proposed anomaly detection model, using the UNSW-NB15 and CICIDS2017 datasets, tracks network traffic through fog nodes to identify cyber-attacks. The conclusion underscores the significance of automation and smart city systems while acknowledging the increasing threat of cyber-attacks, endorsing the use of ensemble models for improved cybersecurity in such contexts [35]. On a separate front, the battle against electricity theft in smart grids takes center stage in another research endeavor. The study puts forth a Machine Learning Boosting Classifiers-based Stacking Ensemble Model (MLBCSM) as a robust solution for detecting non-technical losses (NTLs) [48, 64]. Recognizing the pivotal role of machine learning and deep learning algorithms in enhancing NTL detection accuracy in smart grids, the researchers navigate through challenges like detection accuracy and false positive rates associated with existing models. The proposed MLBCSM, a fusion of five boosting classifiers and an Adaptive Synthetic Sampling technique (ADASYN) to tackle data imbalance, outshines standalone models. The classification approach employs a stacking ensemble strategy, demonstrating remarkable performance in terms of ROC-AUC and PR-AUC values. The research concludes by underscoring the MLBCSM's advantages, including reduced false negative rates, low false positive rates, and improved performance compared to baseline models like AdaBoost [48]. Shifting the focus to real-time event detection in smart cities, a pioneering framework emerges, leveraging sentiment analysis and event detection data from social media platforms like Twitter [40]. This framework, motivated by the escalating real-time reactions of social media users in smart city contexts, offers a nuanced approach to detecting events and conducting citizen satisfaction analysis. Employing machine learning techniques, including natural language processing (NLP), the authors establish a risk taxonomy for smart cities, proposing responses to different event categories. A case study rooted in Cardiff City Council's smart city roadmap provides practical insights into scenarios related to smart street lighting, parking, transportation, and the environment. The methodological approach, characterized by an optimized data pipeline, data sourcing, classification methods, and statistical significance evaluation, enriches the understanding of relationships between variables, thereby contributing valuable insights for smart city decision-making. The comprehensive analysis of the selected literature underscores the revolutionary role of machine learning in advancing smart city development. By categorizing the research into key themes such as transportation systems, flow tracking, green innovation, and security, this review elucidates the multifaceted applications and benefits of machine learning techniques. These insights demonstrate how machine learning not only addresses urban challenges but also enhances the efficiency, sustainability, and safety of smart cities. Furthermore, the incorporation of cutting-edge technologies into urban planning and management highlights the ongoing evolution of smart city concepts. The insights from this review offer a solid basis for future research and development, emphasizing the need for continued innovation and interdisciplinary collaboration. By leveraging data-driven approaches and fostering adaptive city planning, smart cities can achieve their full potential in creating sustainable, livable urban environments for the growing global population.