Physical Layer Message Prediction for 5G Radio Access Network Protocols

TL;DR

This paper introduces a Transformer-based framework for predicting physical layer messages in 5G NR, aiding protocol reverse engineering and security analysis by leveraging standard context and syntax.

Contribution

It presents a novel application of Transformer models to predict 5G physical layer messages, enhancing reverse engineering capabilities.

Findings

Effective message prediction at the physical layer

Utilizes open-source 5G system data for training

Improves understanding of 5G protocol communications

Abstract

Protocol reverse engineering stands as the cutting-edge approach in security research. This paper presents a framework capable of reverse engineering the communications within a mobile communication system. Our focus is on systems released by the 3GPP, with an emphasis on 5G NR. Our approach leverages the available context and syntax of the 5G standard to predict subsequent messages. This approach relies on a Transformer model and is trained based on an open-source 5G system implementation, emulating a base station and several user equipments. The prediction targets messages at the physical layer.