GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments

Zhang, Yawei; Fang, Lanting; Yao, Di; Feng, Kaiyu; Wang, Shuliang

doi:10.24963/ijcai.2026/233

GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments

Yawei Zhang, Lanting Fang, Di Yao, Kaiyu Feng, Shuliang Wang

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 2091-2099.

https://doi.org/10.24963/ijcai.2026/233

Keywords

Machine Learning: Open-World/Open-Set/OOD Learning
Computer Vision: Representation learning
Machine Learning: Time series and data streams
Machine Learning: Feature extraction, selection and dimensionality reduction
Data Mining: Anomaly/outlier detection

Abstract

Physical-layer device identification serves as a critical security mechanism for mitigating cyberattacks and enhancing network resilience. However, its deployment in modern full-duplex Ethernet networks faces two major challenges: (1) the presence of Out-of-Distribution (OOD) data caused by temporal distribution shift and diverse local transmitters, and (2) the difficulty of environment-agnostic device identification, where the unique features of target devices are often obscured in mixed signals. To address these challenges, we propose GenID, which consists of two core components. First, GenID constructs prototypes to emulate the full communication environment through a two stage process: offline and online. Based on the prototype context, it then introduces an invariant feature extraction method to disentangle mixed signals and improve generalization under OOD conditions.Extensive experiments demonstrate that GenID significantly outperforms state-of-the-art baselines, achieving an increase of at least 7.94\% in Macro-F1 score under OOD environments. Our code is available on https://github.com/zyw2004/GenID_IJCAI2026.

Yawei Zhang, Lanting Fang, Di Yao, Kaiyu Feng, and Shuliang Wang. 2026. GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 2091–2099. https://doi.org/10.24963/ijcai.2026/233

@inproceedings{ijcai2026p233,
  title     = {GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments},
  author    = {Zhang, Yawei and Fang, Lanting and Yao, Di and Feng, Kaiyu and Wang, Shuliang},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {2091--2099},
  year      = {2026},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2026/233},
  url       = {https://doi.org/10.24963/ijcai.2026/233},
}

TY  - CPAPER
TI  - GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments
AU  - Zhang, Yawei
AU  - Fang, Lanting
AU  - Yao, Di
AU  - Feng, Kaiyu
AU  - Wang, Shuliang
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 2091
EP  - 2099
DO  - 10.24963/ijcai.2026/233
UR  - https://doi.org/10.24963/ijcai.2026/233
ER  -