Enhancing AI-powered Data Security and Anomaly Detection in 5G Mobile Edge Computing

Abstract

As 5G mobile edge computing gains more attention, ensuring data security and detecting anomalies are becoming increasingly challenging. However, incorporating AI-based solutions can be a promising solution to these challenges. By utilizing machine learning algorithms and predictive models, organizations can significantly enhance their data security and anomaly detection capabilities. By integrating AI-based solutions into 5G mobile edge computing, organizations can monitor and analyze network traffic patterns in real-time. This real-time analysis helps organizations take proactive actions by quickly detecting potential threats and anomalies. Furthermore, AI-based solutions can help detect and prevent data breaches by analyzing user behavior patterns and blocking unauthorized access. By leveraging AI-based solutions, data security and anomaly detection capabilities can be greatly improved. Therefore, organizations should consider utilizing these solutions to protect their systems and data while providing uninterrupted service to customers. This paper explores the NWDAF network function that can be utilized for AI in a 5G mobile edge computing environment. We present appropriate data protection architectures using Intel SGX and simulate them to perform anomaly detection.