Stealthy Backdoor Attack on RF Signal Classification

Tianming Zhao, Zijie Tang, Tianfang Zhang, Huy Phan, Yan Wang, Cong Shi, Bo Yuan, Yingying Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recently, deep learning (DL) has become one of the key technologies supporting radio frequency (RF) signal classification applications. Given the heavy DL training requirement, adopting outsourced training is a practical option for RF application developers. However, the outsourcing process exposes a security vulnerability that enables a backdoor attack. While backdoor attacks have been explored in the computer vision domain, it is rarely explored in the RF domain. In this work, we present a stealthy backdoor attack that targets DL-based RF signal classification. To realize such an attack, we extensively explore the characteristics of the RF data in different applications, which include RF modulation classification and RF fingerprint-based device identification. Particularly, we design a training-based backdoor trigger generation approach with an optimization procedure that not only accommodates dynamic application inputs but also is stealthy to RF receivers. Extensive experiments on two RF signal classification datasets show that the average attack success rate of our backdoor attack is over 99.2%, while its classification accuracy for the clean data remains high (i.e., less than a 0.6% drop compared to the clean model). Additionally, we demonstrate that our attack can bypass existing defense strategies, such as Neural Cleanse and STRIP.

Original languageEnglish (US)
Title of host publicationICCCN 2023 - 2023 32nd International Conference on Computer Communications and Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350336184
StatePublished - 2023
Event32nd International Conference on Computer Communications and Networks, ICCCN 2023 - Honolulu, United States
Duration: Jul 24 2023Jul 27 2023

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
ISSN (Print)1095-2055


Conference32nd International Conference on Computer Communications and Networks, ICCCN 2023
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software


  • Deep Learning Security
  • Mobile Security
  • Radio-Frequency Backdoor Attack
  • Wireless Communication Security


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