Anomaly Detection Against Fake Base Station Threats Using Machine Learning

Abstract

Mobile networking in 4G and 5G remains vulnerable against fake base stations. A fake base station can inject and manipulate the radio resource control (RRC) communication protocol to disable the user equipment’s connectivity. To motivate our research, we empirically show that such a fake base station can cause an indefinite hold of the user equipment’s connectivity using our fake base station prototype against an off-the-shelf phone. To defend against such threat, we design and build an anomaly detection system to detect the fake base station threats. It detects any base station’s deviations from the 4G/5G RRC protocol, which supports both the connectivity provision case (all works well and the user receives connectivity) and the connection-release case (cannot provide connectivity at the time and thus releases connections). Our scheme based on unsupervised machine learning dynamically and automatically controls and sets the detection parameters, which vary with mobility and the communication channel, and utilizes greater information to improve its effectiveness. Using software-defined radios and srsRAN, we implement a prototype of our scheme from sensing to data collection to machine-learning-based detection processing. Our empirical evaluations demonstrate the detection effectiveness and adaptability; i.e., our scheme accurately detects fake base stations deviating from the set protocol in mobile scenarios by adapting its model parameters. Our scheme achieves 100% accuracy in static scenarios against the fake base station threats. If the dynamic control is disabled, i.e., not adapting to mobility and different channel environments, the accuracy drops to 65–76%, but our scheme adjusts the model via dynamic training to recover to 100% accuracy.