Robust and Precise Application Fingerprinting on 5G Physical Uplink Channel

Abstract: Air fingerprinting infers application activity by sniffing metadata from cellular control channels. 5G encrypts these channels, breaking the attack chain that prior attacks depend on. This paper reveals a physical-layer side channel that bypasses encryption: under the link adaptation mandated by the cellular communication standard, the uplink Modulation and Coding Scheme (MCS) remains stable, so the number of Physical Resource Blocks (PRBs) occupied by a transmission accurately reflects the IP packet length. Combined with the uplink control channel that carries downlink information, an attacker can reconstruct a bidirectional traffic profile. This bidirectional information recovery can be achieved simply by observing the uplink spectrum, without decoding any channel. Building on this side channel, we design Crosshair, a passive three-step attack. First, a blind extraction stage recovers the uplink physical channel occupancy from raw IQ samples via energy detection, reconstructing bidirectional traffic from uplink spectrum. Second, we design a data augmentation method that synthesizes spectral profiles across diverse channel conditions, eliminating the need for prior knowledge of the communication environment. Third, cross-modal alignment embeds the spectral and IP domains into a shared space, enabling new applications to be enrolled from a collected IP trace alone. Extensive experiments on a 5G NR testbed demonstrate the robustness and precision of Crosshair: it outperforms the State-of-the-Art (SOTA) physical layer fingerprinting method in application recognition accuracy, and maintains high accuracy in cross-MCS scenarios.