Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting


Zhao, Tianya, Wang, Ningning, Zhang, Junqing ORCID: 0000-0002-3502-2926 and Wang, Xuyu (2025) Protocol-Agnostic and Data-Free Backdoor Attacks on Pre-Trained Models in RF Fingerprinting. In: IEEE INFOCOM 2025 - IEEE Conference on Computer Communications, 2025-5-19 - 2025-5-22.

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Abstract

While supervised deep neural networks (DNNs) have proven effective for device authentication via radio frequency (RF) fingerprinting, they are hindered by domain shift issues and the scarcity of labeled data. The success of large language models has led to increased interest in unsupervised pre-trained models (PTMs), which offer better generalization and do not require labeled datasets, potentially addressing the issues mentioned above. However, the inherent vulnerabilities of PTMs in RF fingerprinting remain insufficiently explored. In this paper, we thoroughly investigate data-free backdoor attacks on such PTMs in RF fingerprinting, focusing on a practical scenario where attackers lack access to downstream data, label information, and training processes. To realize the backdoor attack, we carefully design a set of triggers and predefined output representations (PORs) for the PTMs. By mapping triggers and PORs through backdoor training, we can implant backdoor behaviors into the PTMs, thereby introducing vulnerabilities across different downstream RF fingerprinting tasks without requiring prior knowledge. Extensive experiments demonstrate the wide applicability of our proposed attack to various input domains, protocols, and PTMs. Furthermore, we explore potential detection and defense methods, demonstrating the difficulty of fully safeguarding against our proposed backdoor attack.

Item Type: Conference Item (Unspecified)
Uncontrolled Keywords: 46 Information and Computing Sciences, 4611 Machine Learning, 4604 Cybersecurity and Privacy
Divisions: Faculty of Science and Engineering
Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 04 Mar 2025 08:29
Last Modified: 18 Sep 2025 21:51
DOI: 10.1109/infocom55648.2025.11044704
Related Websites:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3190642
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