Devilray: A Systematic Adversarial Model Revealing Blind Spots in Fake Base Station Detection

TL;DR

Devilray is a systematic adversarial framework that reveals blind spots in fake base station detection by exploring a wide range of realistic adversarial behaviors grounded in real-world data and standards.

Contribution

The paper introduces Devilray, the first robust adversarial model based on real-world analysis, enabling comprehensive evaluation of FBS detectors against realistic adversarial instances.

Findings

All seven evaluated detectors have coverage gaps.

Devilray can generate 2,592 feasible FBS instances.

The study uncovers blind spots rooted in design assumptions.

Abstract

Fake Base Station (FBS) detection has been a critical focus of cellular security research for over two decades. However, significant financial and regulatory barriers to accessing commercial FBS (C-FBS) devices have limited direct visibility into real-world operations, forcing detection systems to be designed and evaluated around self-built prototypes. In this paper, we present Devilray, a reconfigurable and reference-grade adversarial baseline designed to systematically explore the realistic adversarial space and identify adversarial blind spots in current detection -- regions of realistic adversarial behavior excluded by prevailing threat models. We establish an empirical ground truth through the first academic analysis of a C-FBS and extend these observations into specification-driven operational variants permitted by 3GPP standards. Devilray enables the systematic exploration of 2,592 feasible and realistic FBS instances, capturing a wide range of operational possibilities. Using Devilray, we evaluate seven representative accessible FBS detectors and uncover coverage gaps across all seven, revealing blind spots rooted in assumption-bound design and evaluation. Our work provides the first robust adversarial model grounded in real-world behavior and specification analysis, enabling the community to develop and evaluate future detection mechanisms in a rigorous manner.