{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T18:40:03Z","timestamp":1755974403112,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3305202"],"award-info":[{"award-number":["2023YFC3305202"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,13]]},"DOI":"10.1145\/3674700.3674705","type":"proceedings-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T18:26:20Z","timestamp":1725042380000},"page":"27-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Unsupervised Learning-Based Multivariate Anomaly Detection Method for Dynamic Attention Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1176-2560","authenticated-orcid":false,"given":"DunHuang","family":"Shi","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Xi'an Technological University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4648-0436","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Xi'an Technological University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8924-2711","authenticated-orcid":false,"given":"Lei","family":"Sun","sequence":"additional","affiliation":[{"name":"Aerospace Zhirong Information Technology (Zhuhai) Co., Ltd., China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Financial fraud: a review of anomaly detection techniques and recent advances[J]. Expert systems With applications","author":"Hilal W","year":"2022","unstructured":"Hilal W, Gadsden S A, Yawney J. Financial fraud: a review of anomaly detection techniques and recent advances[J]. Expert systems With applications, 2022, 193: 116429."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-023-00470-w"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105503"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106623"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-023-00221-2"},{"key":"e_1_3_2_1_6_1","volume-title":"Anomaly detection of breast cancer using deep learning[J]. Arabian journal for science and engineering","author":"Alloqmani A","year":"2023","unstructured":"Alloqmani A, Abushark Y B, Khan A I. Anomaly detection of breast cancer using deep learning[J]. Arabian journal for science and engineering, 2023, 48(8): 10977-11002."},{"key":"e_1_3_2_1_7_1","volume-title":"A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions[J]","author":"Yan P","year":"2024","unstructured":"Yan P, Abdulkadir A, Luley P P, A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions[J]. IEEE Access, 2024."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2023.026712"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2022.102983"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Li Gen and Jason J. Jung. \"Deep learning for anomaly detection in multivariate time series: Approaches applications and challenges.\"\u00a0Information Fusion\u00a091 (2023): 93-102.","DOI":"10.1016\/j.inffus.2022.10.008"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109084"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105964"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3260563"},{"key":"e_1_3_2_1_14_1","volume-title":"Graph neural network-based anomaly detection in multivariate time series[C]\/\/Proceedings of the AAAI conference on artificial intelligence","author":"Deng A","year":"2021","unstructured":"Deng A, Hooi B. Graph neural network-based anomaly detection in multivariate time series[C]\/\/Proceedings of the AAAI conference on artificial intelligence. 2021, 35(5): 4027-4035."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Su Ya \"Robust anomaly detection for multivariate time series through stochastic recurrent neural network.\"\u00a0Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019.","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_1_16_1","volume-title":"Unsupervised anomaly detection on multivariate time series.\"\u00a0Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","author":"Audibert","year":"2020","unstructured":"Audibert, Julien, \"Usad: Unsupervised anomaly detection on multivariate time series.\"\u00a0Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2020."},{"key":"e_1_3_2_1_17_1","unstructured":"Zong Bo \"Deep autoencoding gaussian mixture model for unsupervised anomaly detection.\"\u00a0International conference on learning representations. 2018."},{"key":"e_1_3_2_1_18_1","first-page":"2118","article-title":"Unsupervised deep anomaly detection for multi-sensor time-series signals","volume":"2","author":"Zhang","year":"2021","unstructured":"Zhang, Yuxin, \"Unsupervised deep anomaly detection for multi-sensor time-series signals.\"\u00a0IEEE Transactions on Knowledge and Data Engineering\u00a035.2 (2021): 2118-2132.","journal-title":"\u00a0IEEE Transactions on Knowledge and Data Engineering\u00a035"},{"key":"e_1_3_2_1_19_1","volume-title":"10-48550","author":"Velickovic","year":"2017","unstructured":"Velickovic, Petar, \"Graph attention networks.\"\u00a0stat\u00a01050.20 (2017): 10-48550."},{"issue":"1","key":"e_1_3_2_1_20_1","first-page":"36","article-title":"Hasibul Alam Ratul, Md. Mahidur Rahman, Ishrat Jahan Diya, and Zunayeed-Bin Zahir, \"Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System","volume":"13","author":"Md","year":"2022","unstructured":"Md. Badiuzzaman Pranto, Md. Hasibul Alam Ratul, Md. Mahidur Rahman, Ishrat Jahan Diya, and Zunayeed-Bin Zahir, \"Performance of Machine Learning Techniques in Anomaly Detection with Basic Feature Selection Strategy - A Network Intrusion Detection System,\" Journal of Advances in Information Technology, Vol. 13, No. 1, pp. 36-44, February 2022.","journal-title":"Journal of Advances in Information Technology"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.4304\/jait.3.3.147-154"}],"event":{"name":"ICCCV 2024: 2024 the 6th International Conference on Control and Computer Vision","acronym":"ICCCV 2024","location":"Tianjin China"},"container-title":["Proceedings of the 2024 6th International Conference on Control and Computer Vision"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674700.3674705","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3674700.3674705","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T18:16:54Z","timestamp":1755973014000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674700.3674705"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,13]]},"references-count":21,"alternative-id":["10.1145\/3674700.3674705","10.1145\/3674700"],"URL":"https:\/\/doi.org\/10.1145\/3674700.3674705","relation":{},"subject":[],"published":{"date-parts":[[2024,6,13]]},"assertion":[{"value":"2024-08-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}