Anomaly Detection in 5G using Variational Autoencoders

With the rapid expansion of 5G networks, ensuring the security and integrity of data transmission becomes paramount. In this context, anomaly detection plays a critical role in identifying suspicious activities or deviations from normal behavior. This paper presents an approach to anomaly detection in 5G Non-IP Data Delivery (NIDD) datasets using Variational Autoencoders (VAE). We use VAE to build an unsupervised machine learning to enable the detection of unknown and previously unseen threats. The VAE is trained on 5G-NIDD datasets, capturing the intricate patterns and characteristics of normal network behavior. Subsequently, anomalies are detected by measuring the reconstruction error between the input data and the reconstructed output from the VAE. Experimental results demonstrate the efficacy of the VAE, showcasing its ability to accurately detect anomalies in 5G-NIDD datasets. This research contributes to the advancement of anomaly detection techniques in 5G networks, enhancing the security and reliability of data transmission in complex network environments.