{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:06:07Z","timestamp":1772813167724,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:p>With the rapid expansion of the Internet ecology, sensitive information leakage has become a key problem to be solved in the field of network security. However, existing sensitive information detection methods are often difficult to effectively capture the implicit relationships among nodes when facing complex association structures, resulting in insufficient detection accuracy and traceability. To cope with the above problems, this paper constructs a hybrid graph neural network-based sensitive information detection and traceability model (HGNN-SID). First, a graph neural network-based feature extraction model is used to achieve node-level representation learning on large-scale graph structured data, which provides structured representations for subsequent relationship mining by sampling neighborhoods and aggregating structural features. Second, to further discover potential higher-order dependencies between nodes, the model introduces a graph attention network, which assigns adaptive weights to different neighbors through the attention mechanism, enabling the model to extract key interaction features in complex propagation networks. Finally, the joint modeling of feature representation and attention relationship is combined to achieve the identification of anomalous propagation paths and the precise identification of risky nodes. Simulation results show that HGNN-SID achieves over 98% accuracy on mainstream datasets, confirming its effectiveness in sensitive-information detection and traceability.<\/jats:p>","DOI":"10.3233\/faia260011","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:37Z","timestamp":1772792437000},"source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid GraphSAGE\u2013Attention Framework for Sensitive Information Detection and Traceability in Log Graphs"],"prefix":"10.3233","author":[{"given":"Weiyu","family":"Wang","sequence":"first","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]},{"given":"Jinyi","family":"Pan","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]},{"given":"Yi","family":"Su","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]},{"given":"Ningning","family":"Kang","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]},{"given":"Yu","family":"Su","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]},{"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Sichuan Ziyang Power Supply Company, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:37Z","timestamp":1772792437000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260011","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}