Nowadays, 5G network deployments and use cases increasingly rely on Artificial Intelligence to enhance network security. Machine Learning models can be leveraged to detect and classify attacks on the network-such as jamming-using Key Performance Indicators (KPIs) like SINR, MCS, and CQI as training data. Another prevalent method involves using spectrograms as training data, effectively transforming the task into an image classification challenge. In dynamic environments like 5G, encompassing every possible scenario when collecting training data is impractical. Hence, it's crucial to develop resilient models that maintain high accuracy when exposed to data of a different distribution in the real world-a concept known as Out-of-Distribution (OOD) Generalization.

Cellular networks, renowned for their robustness and high availability, must consistently meet very stringent standards to ensure service provision and attract deployment in manufacturing industries that cannot tolerate any downtime. Despite the progressive introduction of numerous algorithms over the years across different layers of the 5G communication model to ensure packet delivery and decoding, jamming attacks have consistently disrupted connectivity. Detection and analysis of jamming attack characteristics, including their type and duration, provide comprehensive insights for security analysts to develop educated countermeasures.