Security Enhancement Technologies for 5G Private Networks in Smart Factories with Remote Access

With the increasing reliance on 5G private networks in smart factory environments, security has become a critical concern. This paper presents a method to enhance security through real-time monitoring and machine learning-based anomaly detection for remote system access. By addressing issues such as failed login attempts and other suspicious activities, this approach provides 5G private network operators with tools to strengthen monitoring capabilities, protect critical operations, and ensure the integrity of smart factory systems. The proposed framework is designed to continuously authenticate and authorize access requests in real-time, improving the resilience of smart factories against evolving cyber threats.