{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T22:17:52Z","timestamp":1794003472251,"version":"4.8.0"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202302"],"award-info":[{"award-number":["62202302"]}],"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":["U21B2019"],"award-info":[{"award-number":["U21B2019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3541890","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T13:34:33Z","timestamp":1739453673000},"page":"2266-2280","source":"Crossref","is-referenced-by-count":46,"title":["FeCoGraph: Label-Aware Federated Graph Contrastive Learning for Few-Shot Network Intrusion Detection"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9703-6911","authenticated-orcid":false,"given":"Qinghua","family":"Mao","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5303-1191","authenticated-orcid":false,"given":"Xi","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-387X","authenticated-orcid":false,"given":"Wenchao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4762-2715","authenticated-orcid":false,"given":"Yuxin","family":"Qi","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9863-5404","authenticated-orcid":false,"given":"Xiu","family":"Su","sequence":"additional","affiliation":[{"name":"Big Data Institute, Central South University, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3913-5001","authenticated-orcid":false,"given":"Gaolei","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-3973","authenticated-orcid":false,"given":"Jianhua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Institute of Cyber Science and Technology, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"issue":"3","key":"ref1","first-page":"35","article-title":"Signature based intrusion detection system using SNORT","volume":"1","author":"Kumar","year":"2012","journal-title":"Int. J. Comput. Appl. Inf. Technol."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2970501"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05829-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"ref5","article-title":"Automating botnet detection with graph neural networks","author":"Zhou","year":"2020","journal-title":"arXiv:2003.06344"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100747"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS54207.2022.9789878"},{"key":"ref8","article-title":"Graph-based solutions with residuals for intrusion detection: The modified E-GraphSAGE and E-ResGAT algorithms","author":"Chang","year":"2021","journal-title":"arXiv:2111.13597"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3213807"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3228493"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110030"},{"key":"ref12","article-title":"Deep graph infomax","author":"Veli\u010dkovi\u0107","year":"2018","journal-title":"arXiv:1809.10341"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110966"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3290650"},{"issue":"2","key":"ref16","first-page":"187","article-title":"IDS classification algorithm based on fuzzy SVM model","volume":"37","author":"Sheng","year":"2018","journal-title":"Appl. Res. Comput."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102158"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6634811"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI44817.2019.9003126"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSUSC.2018.2793284"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.069"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CBD.2014.41"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TrustCom56396.2022.00069"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2991876"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3042049"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3233\/jifs-200694"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761115"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3318960"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref32","article-title":"Learning deep representations by mutual information estimation and maximization","author":"Hjelm","year":"2018","journal-title":"arXiv:1808.06670"},{"key":"ref33","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. NIPS","author":"Khosla"},{"key":"ref34","article-title":"Deep graph contrastive representation learning","author":"Zhu","year":"2020","journal-title":"arXiv:2006.04131"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"ref36","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume-title":"Proc. NIPS","volume":"33","author":"You"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17206"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524722"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3501815"},{"key":"ref40","article-title":"FedGNN: Federated graph neural network for privacy-preserving recommendation","author":"Wu","year":"2021","journal-title":"arXiv:2102.04925"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20643"},{"key":"ref42","article-title":"Towards federated graph learning for collaborative financial crimes detection","author":"Suzumura","year":"2019","journal-title":"arXiv:1909.12946"},{"key":"ref43","article-title":"FedGraphNN: A federated learning system and benchmark for graph neural networks","author":"He","year":"2021","journal-title":"arXiv:2104.07145"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00060"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26187"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3240291"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/SSP53291.2023.10208058"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CCCI55352.2022.9926623"},{"key":"ref49","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"key":"ref50","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-021-01843-0"},{"key":"ref52","first-page":"1","article-title":"Federated learning with matched averaging","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"issue":"86","key":"ref54","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10884817.pdf?arnumber=10884817","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T06:01:05Z","timestamp":1740636065000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10884817\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3541890","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}