{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:05:24Z","timestamp":1763345124755,"version":"3.45.0"},"reference-count":23,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.067284","type":"journal-article","created":{"date-parts":[[2025,8,5]],"date-time":"2025-08-05T07:42:29Z","timestamp":1754379749000},"page":"1733-1750","source":"Crossref","is-referenced-by-count":0,"title":["DSGNN: Dual-Shield Defense for Robust Graph Neural Networks"],"prefix":"10.32604","volume":"85","author":[{"given":"Xiaohan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yuanfang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Gyu Myoung","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Noel","family":"Crespi","sequence":"additional","affiliation":[]},{"given":"Pierluigi","family":"Siano","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","article-title":"Scientific discovery in the age of artificial intelligence","volume":"620","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"10466","DOI":"10.1109\/TPAMI.2024.3443141","article-title":"A survey on graph neural networks for time series: fforecasting, classification, imputation, and anomaly detection","volume":"46","author":"Jin","year":"2024","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1145\/3655103.3655110","article-title":"Exploring the potential of large language models (llms) in learning on graphs","volume":"25","author":"Chen","year":"2024","journal-title":"ACM SIGKDD Explor Newsletter"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"104216","DOI":"10.1016\/j.jnca.2025.104216","article-title":"Mixer-transformer: adaptive anomaly detection with multivariate time series","volume":"241","author":"Fang","year":"2025","journal-title":"J Netw Comput Appl"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"817","DOI":"10.32604\/cmc.2024.057660","article-title":"DIGNN-A: real-time network intrusion detection with integrated neural networks based on dynamic graph","volume":"82","author":"Liu","year":"2025","journal-title":"Comput Mater Continua"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Chen Y, Fang X, Ma S, Li W. TrackSecurity: an attention-guided physical patch model for perturbing visual trackers in autonomous driving. TechRxiv. Forthcoming 2025. doi:10.36227\/techrxiv.175021860.09534391\/v1.","DOI":"10.36227\/techrxiv.175021860.09534391\/v1"},{"key":"ref7","series-title":"International Conference on Learning Representations","article-title":"Understanding and improving graph injection attack by promoting unnoticeability","author":"Chen","year":"2022"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.32604\/cmc.2024.057814","article-title":"PIAFGNN: property inference attacks against federated graph neural networks","volume":"82","author":"Liu","year":"2025","journal-title":"Comput Mater Continua"},{"key":"ref9","series-title":"Proceedings of the 13th International Conference on Web Search and Data Mining. Houston, TX, USA","first-page":"169","article-title":"All you need is low (rank) defending against adversarial attacks on graphs","author":"Entezari","year":"2020"},{"key":"ref10","series-title":"Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS)","article-title":"GNNGuard: defending graph neural networks against adversarial attacks","author":"Zhang","year":"2020"},{"key":"ref11","series-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019). Macau, China: IJCAI Organization","first-page":"4816","article-title":"Adversarial examples for graph data: deep insights into attack and defense","author":"Wu","year":"2019"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TKDE.2019.2957786","article-title":"Graph adversarial training: dynamically regularizing based on graph structure","volume":"33","author":"Feng","year":"2019","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref13","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, AK, USA","first-page":"1399","article-title":"Robust graph convolutional networks against adversarial attacks","author":"Zhu","year":"2019"},{"key":"ref14","series-title":"The Twelfth International Conference on Learning Representations. Vienna, Austria","article-title":"Bounding the expected robustness of graph neural networks subject to node feature attacks","author":"Abbahaddou","year":"2024"},{"key":"ref15","series-title":"Proceedings of the 5th Workshop on Machine Learning and Systems (EuroMLSys\u201925)","first-page":"168","article-title":"\u03b2-GNN: a robust ensemble approach against graph structure perturbation","author":"Aslan","year":"2025"},{"key":"ref16","series-title":"Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence","first-page":"21063","article-title":"A simple and yet fairly effective defense for graph neural networks","author":"Ennadir","year":"2024 Feb 20\u201327"},{"key":"ref17","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"66","article-title":"Graph structure learning for robust graph neural networks","author":"Jin","year":"2020"},{"key":"ref18","series-title":"The Semantic Web: 15th International Conference, ESWC 2018","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2018 Jun 3\u20137"},{"key":"ref19","first-page":"1115","volume":"80","author":"Dai","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"128761","DOI":"10.1016\/j.neucom.2024.128761","article-title":"A survey of graph neural networks and their industrial applications","volume":"614","author":"Lu","year":"2025","journal-title":"Neurocomputing"},{"key":"ref21","series-title":"ICLR Workshop on Safe Machine Learning (SafeML)","article-title":"Adversarial attacks on graph neural networks via meta learning","author":"Z\u00fcgner","year":"2019"},{"key":"ref22","series-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19)","first-page":"3961","article-title":"Topology attack and defense for graph neural networks: an optimization perspective","author":"Xu","year":"2019"},{"key":"ref23","first-page":"8463","article-title":"Certified robustness of graph convolution networks for graph classification under topological attacks","volume":"33","author":"Jin","year":"2020","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-1\/TSP_CMC_67284\/TSP_CMC_67284.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:01:46Z","timestamp":1763344906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n1\/63563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067284","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}