Real-Time Jamming Detection, Classification and Logging Using Computer Vision in 5G Private Networks

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. This paper demonstrates how computer vision can be employed on spectrograms to detect, classify, and log information on three types of jamming attacks in real-time within a 5G campus network. We evaluate the efficiency of our real-time jamming detector by analyzing detection latency, classification accuracy, and attack duration capture accuracy. Our results indicate that our Deep Learning classification model detects all tested jammers with remarkably high accuracy.