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These vulnerabilities as well as reliability issues can compromise product integrity, safety, and operational continuity, posing severe risks to both industry and national security. In this work, we propose a novel methodology for modeling the AM process chain as a Cyber-Physical System (CPS) using multi-modal data structured in a graph format. Our methodology leverages Graph Neural Networks (GNNs) to detect and localize anomalies across diverse data modalities, enabling precise identification of both the nature and source of attack\/fault. By integrating data fusion, advanced anomaly classification, and localization techniques, our solution provides a robust methodology for enhancing the security and reliability of AM processes, ensuring their safe deployment in critical applications. Furthermore, the proposed technique is adaptable to other industrial systems, underscoring its potential for broader impact in securing critical infrastructure.<\/jats:p>","DOI":"10.1145\/3777457","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T15:39:51Z","timestamp":1763480391000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Deviation Network for Anomaly Detection and Localization in Additive Manufacturing Systems"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5761-9865","authenticated-orcid":false,"given":"Rozhin","family":"Yasaei","sequence":"first","affiliation":[{"name":"The University of Arizona, Tucson, Arizona, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2180-7336","authenticated-orcid":false,"given":"Ashley Sayuri","family":"Masuda","sequence":"additional","affiliation":[{"name":"University of California Irvine, Irvine, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4927-8049","authenticated-orcid":false,"given":"Yasamin","family":"Moghaddas","sequence":"additional","affiliation":[{"name":"University of California Irvine, Irvine, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5390-0497","authenticated-orcid":false,"given":"Mohammad","family":"Abdullah Al Faruque","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science, University of California Irvine, Irvine, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP57164.2023.00071"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPS.2016.7479068"},{"issue":"13","key":"e_1_3_1_4_2","first-page":"176","article-title":"Forensics of thermal side-channel in additive manufacturing systems","volume":"12","author":"Faruque Mohammad Abdullah Al","year":"2016","unstructured":"Mohammad Abdullah Al Faruque, Sujit Rokka Chhetri, A. 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