{"type":"ScholarlyArticle","title":"GenID: A Generalizable Physical-Layer Device Identification Method for Out-of-Distribution Environments","authors":[{"name":"Yawei Zhang","slug":"yawei-zhang","given":"Yawei","family":"Zhang","affiliation":""},{"name":"Lanting Fang","slug":"lanting-fang","given":"Lanting","family":"Fang","affiliation":""},{"name":"Di Yao","slug":"di-yao","given":"Di","family":"Yao","affiliation":""},{"name":"Kaiyu Feng","slug":"kaiyu-feng","given":"Kaiyu","family":"Feng","affiliation":""},{"name":"Shuliang Wang","slug":"shuliang-wang","given":"Shuliang","family":"Wang","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/233","url":"https://ijcai.org/proceedings/2026/233","pdf":"https://ijcai.org/proceedings/2026/0233.pdf","markdown":"https://ijcai.org/proceedings/2026/233.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/233","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":233,"pages":{"first":2091,"last":2099},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}