Vehicular-to-Everything (V2X) communication standards ensure reliable and high-performance data exchange among vehicles, pedestrians, and the roadside infrastructure. 5G New Radio (NR) is a crucial technology that enables Vehicle-to-Network (V2N) and Vehicle-to-Infrastructure (V2I) communications. In the security context, applications and network services that rely on these communication interfaces are subject to external attack sources like radio jamming that target the same control and data frequencies used by them. This causes system and network performance degradation and even Denial of Service (DoS) events, which could lead to traffic accidents involving vehicles and/or Vulnerable Road Users (VRUs). Radio jamming attacks can adopt a smart behavior by changing the targeted center frequency, bandwidth, duration, or time between two consecutive attack bursts over time. Given the context above, we propose in this paper a Deep Learning (DL)-based approach to detect radio jamming attacks on V2I/V2N communication interfaces. Our DL model is trained using a dataset collected from our 5G-V2X testbed. Results show that our DL model outperforms traditional ML algorithms and provides a detection accuracy of up to 96%, a false positive rate of less than 3%, and a detection time decrease of 39% minimum.