Improving airport security with IoT-powered deep learning methods for threat detection and intelligent recommendation systems

Abstract

This study introduces a novel framework that integrates deep learning algorithms, intelligent recommendation systems, and Internet-of-things (IoT) devices to improve airport security. Surveillance cameras are used by the system to identify potentially dangerous activities, such leaving luggage unattended. By analyzing interactions and movement patterns, anomalies can be found, and security staff can receive notifications. To improve security measures based on historical data and current information, it makes use of recommendation systems. This integrated approach reduces false alerts, improves operating efficiency, and strengthens security safeguards. Case studies and simulations confirm the framework's effectiveness. Interestingly, transfer learning using MVCNN architecture detected threats in test data that had never been seen before with an accuracy of above 95%. Additionally, the suggested ISODI approach outperformed Decision Tree and K-Nearest Neighbors in identifying anomalies associated with aircraft delays, with an accuracy of 0.99. These results show how the framework can raise airport security requirements. Notwithstanding the beneficial developments of this paradigm, more investigation is necessary to identify the best use case for it in the security systems in place today and to handle any privacy issues that may arise. To handle the complexities of various airport environments, practical systems would require extensive testing and adaptability.