Rogue Base Station Detection in 5G/6G Vehicular Networks: A Comprehensive V2X-Oriented Taxonomy, Evaluation Framework, and Research Roadmap
Rogue Base Stations (RBS) remain a persistent security threat to fifth-generation (5G) and emerging sixth-generation (6G) cellular systems by impersonating legitimate infrastructure and exploiting vulnerabilities in pre-authentication signaling and mobility procedures. The risk is particularly critical in vehicular and Vehicle-to-Everything (V2X) environments, where high mobility and millisecond-scale handover operations tightly couple communication reliability with safety-critical control functions. Although prior surveys examine LTE identity catchers and general cellular security threats, they rarely evaluate RBS detection under vehicular mobility dynamics or within the latency and reliability constraints of Ultra-Reliable Low-Latency Communication (URLLC) services. In addition, the limited availability of realistic measurement report (MR) datasets have hindered reproducible benchmarking of data-driven detection methods. This article presents a vehicular-oriented survey of RBS detection in 5G and beyond networks, explicitly addressing mobility-constrained detection, handover-security interactions, and V2X safety requirements that are not systematically addressed in prior surveys, which primarily focus on pre-5G threat models, IMSI-catcher attacks, or general cellular security. We introduce a method-centric taxonomy that organizes existing approaches into five families based on their primary evidence sources and inference mechanisms: signal anomaly detection, protocol and traffic analytics, RF fingerprinting, network-level frameworks, and machine-learning-based detection. Using a PRISMA-compatible structured literature review across 102 included studies and a structured comparative evaluation framework, each family is analyzed across detection latency, computational overhead, robustness to mobility, false alarm susceptibility, and feasibility within quantified pre-handover decision windows. Direct cross-study quantitative comparison is precluded by heterogeneous reporting conventions across the surveyed literature; the framework, therefore, provides structured qualitative synthesis and indicative performance ranges rather than pooled empirical estimates. The analysis reveals that no individual technique satisfies vehicular URLLC constraints in isolation, motivating layered architectures combining lightweight UE-side detection with edge-assisted and operator-level analytics. A scenario-driven safety analysis links detection error rates to operational consequences across five V2X use cases under varying URLLC severity levels. The survey formalizes evaluation criteria for MR-driven detection and highlights realistic MR generation as a foundation for reproducible evaluation and cross-study comparison in next-generation vehicular communication systems.