Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture

• URL: http://arxiv.org/abs/2505.17776v1
• Date: Fri, 23 May 2025 11:46:11 GMT
• Title: Sec5GLoc: Securing 5G Indoor Localization via Adversary-Resilient Deep Learning Architecture
• Authors: Ildi Alla, Valeria Loscri,
• Abstract summary: 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges.<n>We formalize a threat model encompassing attackers who inject forged radio signals or perturb channel measurements to mislead the localization system.<n>We propose an adversary-resilient localization architecture that combines deep learning fingerprinting with physical domain knowledge.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging 5G millimeter-wave and sub-6 GHz networks enable high-accuracy indoor localization, but security and privacy vulnerabilities pose serious challenges. In this paper, we identify and address threats including location spoofing and adversarial signal manipulation against 5G-based indoor localization. We formalize a threat model encompassing attackers who inject forged radio signals or perturb channel measurements to mislead the localization system. To defend against these threats, we propose an adversary-resilient localization architecture that combines deep learning fingerprinting with physical domain knowledge. Our approach integrates multi-anchor Channel Impulse Response (CIR) fingerprints with Time Difference of Arrival (TDoA) features and known anchor positions in a hybrid Convolutional Neural Network (CNN) and multi-head attention network. This design inherently checks geometric consistency and dynamically down-weights anomalous signals, making localization robust to tampering. We formulate the secure localization problem and demonstrate, through extensive experiments on a public 5G indoor dataset, that the proposed system achieves a mean error approximately 0.58 m under mixed Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) trajectories in benign conditions and gracefully degrades to around 0.81 m under attack scenarios. We also show via ablation studies that each architecture component (attention mechanism, TDoA, etc.) is critical for both accuracy and resilience, reducing errors by 4-5 times compared to baselines. In addition, our system runs in real-time, localizing the user in just 1 ms on a simple CPU. The code has been released to ensure reproducibility (https://github.com/sec5gloc/Sec5GLoc).

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