{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T05:57:30Z","timestamp":1716443850356},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"R&D Program of Beijing Municipal Education Commission","award":["KM202211232012"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challenges in artificial intelligence of things (AIoT) applications owing to their limited flexibility. These challenges arise from the distributed nature of AIoT devices and the involved information, as well as the requirement for short end-to-end latency. To address these challenges, we propose a security authentication scheme that utilizes intelligent prediction mechanisms to detect spoofing attack. Our approach is based on a dynamic authentication method using long short term memory (LSTM), where the edge computing node observes and predicts the time-varying channel information of access devices to detect clone nodes. Additionally, we introduce a Savitzky\u2013Golay filter-assisted high order cumulant feature extraction model (SGF-HOCM) for preprocessing channel information. By utilizing future channel attributes instead of relying solely on previous channel information, our proposed approach enables authentication decisions. We have conducted extensive experiments in actual industrial environments to validate our prediction-based security strategy, which has achieved an accuracy of 97%.<\/jats:p>","DOI":"10.3390\/s23156711","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T06:14:48Z","timestamp":1690438488000},"page":"6711","source":"Crossref","is-referenced-by-count":1,"title":["Channel Prediction-Based Security Authentication for Artificial Intelligence of Things"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaoying","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, Beijing 100192, China"}]},{"given":"Jinwei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Wenying","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, Beijing 100192, China"}]},{"given":"Guangda","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, Beijing 100192, China"}]},{"given":"Xuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Management, Beijing Information Science & Technology University, Beijing 100192, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhong, A., Li, Z., Wu, D., Tang, T., and Wang, R. 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