Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting
This work addresses security risks in device authentication systems for wireless communications, revealing a novel attack vector that is difficult to detect and defend against, though it is incremental in exploring vulnerabilities in a specific domain.
The paper tackles the vulnerability of unsupervised pre-trained models in RF fingerprinting to data-free backdoor attacks, where attackers implant triggers without access to downstream data, achieving successful attacks across various protocols and models as demonstrated in experiments.
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.