Passive monitoring of 5G New Radio (NR) traffic is well known to be very difficult because of the high configurability of physical layer settings and the presence of important but hidden parameters such as scrambling ID’s and Radio Network Temporary Identifiers (RNTIs). In this demonstration we show a real-time Golden Sniffer, an optimized C++ implementation which is able to recover user-specific parameters on the spot without any prior knowledge. The demo features an SDR-based live gNB, COTS devices, and our sniffer working in real time to extract cell metadata and to identify the Scrambling IDs and RNTIs by blind decoding of the Downlink Control Information (DCI). A dashboard displays the ongoing extraction of active RNTIs as well as real-time estimates of uplink and downlink throughput.
Mangione, S., Palamà, I., Dino, A., Focarelli, G., Bianchi, G. (2026). Demo: Real-Time Golden Sniffer for 5G NR Signal Interception. In Proceedings ACM MOBICOM (pp. 1855-1857) [10.1145/3795866.3848476].
Demo: Real-Time Golden Sniffer for 5G NR Signal Interception
S. Mangione
;A. Dino;
2026-10-26
Abstract
Passive monitoring of 5G New Radio (NR) traffic is well known to be very difficult because of the high configurability of physical layer settings and the presence of important but hidden parameters such as scrambling ID’s and Radio Network Temporary Identifiers (RNTIs). In this demonstration we show a real-time Golden Sniffer, an optimized C++ implementation which is able to recover user-specific parameters on the spot without any prior knowledge. The demo features an SDR-based live gNB, COTS devices, and our sniffer working in real time to extract cell metadata and to identify the Scrambling IDs and RNTIs by blind decoding of the Downlink Control Information (DCI). A dashboard displays the ongoing extraction of active RNTIs as well as real-time estimates of uplink and downlink throughput.| File | Dimensione | Formato | |
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