{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:50:26Z","timestamp":1784217026065,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871232"],"award-info":[{"award-number":["61871232"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYLX16_0650"],"award-info":[{"award-number":["KYLX16_0650"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["61871232"],"award-info":[{"award-number":["61871232"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYLX16_0650"],"award-info":[{"award-number":["KYLX16_0650"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional security measures often fail to counter sophisticated threats and complex attacks. To tackle these difficulties, the current study introduces an attention-enhanced defensive distillation network (AEDDN) to improve robustness and accuracy in V2X mm-wave communication under adversarial attacks. The AEDDN model combines the transformer algorithm with defensive distillation, leveraging the transformer\u2019s attention mechanism to focus on critical channel features and adapt to complex conditions. This helps mitigate adversarial examples by filtering misleading data. Defensive distillation further strengthens the model by smoothing decision boundaries, making it less sensitive to small perturbations. To evaluate and validate the AEDDN model, this study uses a publicly available dataset called 6g-channel-estimation and a proprietary dataset named MMMC, comparing the simulation results with the convolutional neural network (CNN) model. The findings from the experiments indicate that the AEDDN, especially in the complex V2X mm-wave environment, demonstrates enhanced performance.<\/jats:p>","DOI":"10.3390\/s24196464","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:30:18Z","timestamp":1728286218000},"page":"6464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Attention-Enhanced Defensive Distillation Network for Channel Estimation in V2X mm-Wave Secure Communication"],"prefix":"10.3390","volume":"24","author":[{"given":"Xingyu","family":"Qi","sequence":"first","affiliation":[{"name":"College of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjian","family":"Liu","sequence":"additional","affiliation":[{"name":"Office for First-Class Disciplines and High-Level University Construction, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchun","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chataut, R., and Akl, R. 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