Cellular networks are the backbone of today's connected world, supporting real-time communication, commerce, transportation, emergency response, and daily life. As fifth-generation (5G) and future wireless systems become more advanced and programmable, they also introduce greater complexity and new attack surfaces. Control-plane signaling systems, responsible for connection setup, identity verification, authentication, key exchange, and mobility, are especially critical, as attacks on them can disrupt services, enable tracking, intercept communications, and expose sensitive data. Despite their importance, defenders often lack realistic, trustworthy data to study these threats because much of the device-side software is proprietary, and network infrastructure is difficult to access. This project addresses these challenges by creating privacy-preserving, verifiable datasets that capture real-world control-plane behavior and attacks, enabling stronger and more effective security research. The project develops new methods for collecting, validating, sanitizing, and analyzing control-plane data across user devices, radio access networks, and core systems. It introduces an in-device observability framework to capture security-relevant events and a hybrid replay environment that uses real-world traces to enable large-scale, realistic control-plane data collection of Open Radio Access Networks (Open RAN) and core networks. Privacy-preserving transformations and synthetic data gen