5G and beyond cellular systems embrace the disaggregation of Radio Access Network (RAN) components, exemplified by the evolution of the fronthual (FH) connection between cellular baseband and radio unit equipment. Crucially, synchronization over the FH is pivotal for reliable 5G services. In recent years, there has been a push to move these links to an Ethernet-based packet network topology, leveraging existing standards and ongoing research for Time-Sensitive Networking (TSN). However, TSN standards, such as Precision Time Protocol (PTP), focus on performance with little to no concern for security. This increases the exposure of the open FH to security risks. Attacks targeting synchronization mechanisms pose significant threats, potentially disrupting 5G networks and impairing connectivity. In this paper, we demonstrate the impact of successful spoofing and replay attacks against PTP synchronization. We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack, necessitating manual intervention to restore full network operations. To counter this, we design a Machine Learning (ML)-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.

Abstract--Spectrum sharing allows different protocols of the same standard (e.g., 802.11 family) or different standards (e.g., LTE and DVB) to coexist in overlapping frequency bands. Third, it utilizes an extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training, which is released along with the code for community use. Legacy protocol detection methods are integrated into the RF receiver network interface card, enabling fast preamble I. However, updating the system for new protocols leads to backward compatibility issues and The increasing demand for wireless services has caused a potential detection errors in challenging wireless channels. This results in congested the other hand, software-defined radio (SDR) systems offer wireless spectrum environments as various communication flexibility but introduce higher latencies due to data transfer protocols coexist in the same frequency bands [2]. This paper transmissions further raise security concerns, posing demonstrates that using edge devices with CPU and GPU risks to critical operations [3]. Detecting diverse protocols in on a system-on-a-module (SOM), along with careful software crowded spectrums allows for intelligent strategies to mitigate design, can overcome the these limitations and enable realtime interference, improve spectral efficiency, and enhance overall processing for complex ML wireless applications on an wireless system performance, benefiting regulatory bodies, SDR-based edge device.