## 4 Discussion  
The implementation of IoT-based intelligent surveillance systems and deep learning models has been redefining public security over the past decade, enabling real-time monitoring in both cyberspace and physical environments within smart cities. Digitalization and population growth have introduced new challenges in crime and violence detection, prompting governments to adopt innovative and automated solutions to enhance urban and digital security. China and India have emerged as leaders in this transformation, deploying large-scale AI-driven surveillance networks and IoT technologies. In China, facial recognition and behavioral analysis play a crucial role in the early detection of suspicious activities, whereas India has prioritized the implementation of IoT security infrastructures to safeguard public spaces and improve incident response capabilities. These strategies not only optimize security resources but also establish new global standards for automated citizen monitoring. For developing countries, adopting these technologies presents a key opportunity to address high crime and violence rates through AI-based solutions that can complement or enhance existing security systems, as seen in Colombia, Mexico, and Chile. In Brazil, the \"Smart Sampa\" project [69] focuses on intrusion detection, theft prevention, facial recognition, and license plate reading, demonstrating an integrated approach to urban security. In Mexico, \"Ciudad Segura\" [70] implements panic buttons, emergency dispatch systems, and intelligent video surveillance, with a particular focus on women's safety in public spaces. Colombia's \"Medellin Ciudad Segura\" [71] advances video surveillance and alert management to enable rapid incident response. Meanwhile, Chile's \"Piloto Seguridad Pudahuel\" [72] concentrates on gunshot detection to control armed violence, illustrating how AI can be leveraged to address specific security issues. A web search revealed that most countries in this region lack flagship projects utilizing AI for crime management, highlighting the need for crime detection models tailored to smart cities. The need for highly precise and robust models is essential to ensure the effective detection of critical events such as fights, armed assaults, or terrorist attacks. However, a literature review revealed that only 17% of the analyzed studies propose solutions focused on violence and crime detection in physical environments. In contrast, 76% of the studies focus on cybersecurity, addressing intrusion detection, DDoS attacks, data manipulation, and vulnerabilities in IoT networks. This indicates a greater concern in the scientific community for protecting digital infrastructures, leaving an opportunity to develop deep learning applications for crime monitoring in urban spaces. Studies have demonstrated that the most effective models for intrusion detection and IoT cybersecurity are those that combine deep learning with hyperparameter optimization and hybrid approaches. Techniques such as DNN, CNN, LSTM, and Gradient Boosting algorithms have achieved high accuracy rates, averaging 97.44% in the analyzed studies. However, some models exhibit deficiencies due to imbalanced datasets, insufficient parameter optimization, or approaches that prioritize one metric over another. To mitigate these limitations, several studies have combined deep learning with genetic algorithms, enabling the automatic selection of the most relevant features and fine-tuning of neural network hyperparameters, thereby improving model stability and accuracy in complex urban environments. While IoT and deep learning-based security monitoring systems offer advantages such as 24/7 coverage and automated threat detection, they also introduce new vulnerabilities that can be exploited by malicious actors.