{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:09:31Z","timestamp":1753884571775,"version":"3.41.2"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","funder":[{"DOI":"10.13039\/501100017700","name":"Henan Province Science and Technology Research Project","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p> In an era of big data, accurately detection towards cyber attacks is crucial to retain healthy operation of cyberspace. Traditional detection methods often existed locality when detecting new types of attacks. To deal with this issue, this paper proposes a robust cyber attack detection method through attention-based graph neural network. It introduces channel attention to achieve linear pooling of data, and then fuses graph convolutional network to construct a graph representation model for cyberspace data. The nodes and edges in the network are represented as the structure of the graph, and the attention mechanism is combined to dynamically calculate the importance weight of nodes in each graph convolution layer. The technical roadmap is to utilize the strong feature representation ability of attention-based graph learning to establish a robust cyber attack detection approach. At last, the proposed approach is assessed using the CSIC2010 dataset by comparing with several typical methods. The experimental results show that the proposed method can better identify and distinguish cyber attack types, and has higher accuracy and robustness. This proposal is expected to help cyberspace and operators avoid increasingly complex cyber attacks in practical applications. <\/jats:p>","DOI":"10.1142\/s0218126625501592","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T10:13:04Z","timestamp":1733393584000},"source":"Crossref","is-referenced-by-count":0,"title":["A Robust Cyber Attack Detection Method Through Attention-Based Graph Neural Networks"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5710-1865","authenticated-orcid":false,"given":"Xiangyang","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Cybersecurity, Henan Police College, Zhengzhou 450046, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"S0218126625501592BIB002","doi-asserted-by":"publisher","DOI":"10.3390\/app10228160"},{"key":"S0218126625501592BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03412-8"},{"key":"S0218126625501592BIB004","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16523"},{"key":"S0218126625501592BIB005","first-page":"103399","volume":"72","author":"Liu Y.","year":"2023","journal-title":"J. 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