PHOENIX: Device-Centric Cellular Network Protocol Monitoring using Runtime Verification

Abstract

End-user-devices in the current cellular ecosystem are prone to many different vulnerabilities across different generations and protocol layers. Fixing these vulnerabilities retrospectively can be expensive, challenging, or just infeasible. A pragmatic approach for dealing with such a diverse set of vulnerabilities would be to identify attack attempts at runtime on the device side, and thwart them with mitigating and corrective actions. Towards this goal, in the paper we propose a general and extendable approach called Phoenix for identifying n-day cellular network control-plane vulnerabilities as well as dangerous practices of network operators from the device vantage point. Phoenix monitors the device-side cellular network traffic for performing signature-based unexpected behavior detection through lightweight runtime verification techniques. Signatures in Phoenix can be manually-crafted by a cellular network security expert or can be automatically synthesized using an optional component of Phoenix, which reduces the signature synthesis problem to the language learning from the informant problem. Based on the corrective actions that are available to Phoenix when an undesired behavior is detected, different instantiations of Phoenix are possible: a full-fledged defense when deployed inside a baseband processor; a user warning system when deployed as a mobile application; a probe for identifying attacks in the wild. One such instantiation of Phoenix was able to identify all 15 representative n-day vulnerabilities and unsafe practices of 4G LTE networks considered in our evaluation with a high packet processing speed (∼68000 packets/second) while inducing only a moderate amount of energy overhead (∼4mW).

LTrack: Stealthy Tracking of Mobile Phones in LTE

“…Therefore, to UEs, the behavior and location of the cell are legitimate. As proposed in [11], a signature based anomaly detector with a signature: "if Identity Request, then attack", is successful in the detection of our attack. However, because Identity Requests are also sent during a legitimate protocol flow, such a solution will inherently report false positives during legitimate identification procedures.…”

AdaptOver: Adaptive Overshadowing Attacks in Cellular Networks

“…On a higher layer, separating a legitimate rejection from a bogus one could be possible by identifying the absence of a message authentication code. In [5], Echeverria et al tried to identify an attach, service or authentication reject attack originating from a fake base station, but other than the presence of the reject message, they could not identify any characteristic that would clearly separate the attack from a legitimate rejection.…”

“…In these works, the authors detect fake base stations due to their unusual broadcast configuration, location, or other indicators. In [5], the authors do not use lower layer indicators to detect the presence of a fake base station, but rather rely on the protocol trace of the interaction with the fake base station. Fake Base Station Attack Impact.…”