The increasing security threats in public places such as airports, train stations, and shopping malls require the development of smart security systems that can detect potential threats and provide timely alerts to security personnel. This research paper proposes an IoT-based smart alert network security system using machine learning to enhance public safety. The system integrates various sensors and devices that collect data such as motion, which is analyzed using machine learning algorithms to detect anomalies and trigger alerts if any suspicious activity is detected. The proposed system achieves an accuracy rate of 91.12% in detecting suspicious activities, which is significantly higher than the existing security systems used in public places. The system can provide real-time alerts, which can reduce the response time of security personnel and prevent potential security threats. The proposed system can be implemented in various public places to enhance public safety and prevent security breaches. The results of this research paper provide a useful reference for future studies on the development of smart security systems using IoT and machine learning technologies.
