Zhao, Tianya, Wang, Xuyu, Zhang, Junqing 
ORCID: 0000-0002-3502-2926 and Mao, Shiwen
(2023)
Explanation-Guided Backdoor Attacks on Model-Agnostic RF Fingerprinting.
In: IEEE International Conference on Computer Communications.
|
PDF
INFOCOM2024_BackdoorRFFI.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
Despite the proven capabilities of deep neural networks (DNNs) for radio frequency (RF) fingerprinting, their security vulnerabilities have been largely overlooked. Unlike the extensively studied image domain, few works have explored the threat of backdoor attacks on RF signals. In this paper, we analyze the susceptibility of DNN-based RF fingerprinting to backdoor attacks, focusing on a more practical scenario where attackers lack access to control model gradients and training processes. We propose leveraging explainable machine learning techniques and autoencoders to guide the selection of positions and values, enabling the creation of effective backdoor triggers in a model-agnostic manner. To comprehensively evaluate our backdoor attack, we employ four diverse datasets with two protocols (Wi-Fi and LoRa) across various DNN architectures. Given that RF signals are often transformed into the frequency or time-frequency domains, this study also assesses attack efficacy in the time-frequency domain. Furthermore, we experiment with potential defenses, demonstrating the difficulty of fully safeguarding against our attacks.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 11 Jan 2024 08:46 |
| Last Modified: | 11 Jan 2024 08:46 |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3177798 |
Share
Share
