{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T17:30:51Z","timestamp":1783791051654,"version":"3.55.0"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["62202066"],"award-info":[{"award-number":["62202066"]}],"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":["62102040"],"award-info":[{"award-number":["62102040"]}],"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":["62002028"],"award-info":[{"award-number":["62002028"]}],"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":[[2023]]},"DOI":"10.1109\/tifs.2023.3245413","type":"journal-article","created":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T18:31:18Z","timestamp":1676399478000},"page":"1638-1652","source":"Crossref","is-referenced-by-count":48,"title":["A High Accuracy and Adaptive Anomaly Detection Model With Dual-Domain Graph Convolutional Network for Insider Threat Detection"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2740-3470","authenticated-orcid":false,"given":"Ximing","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5597-9306","authenticated-orcid":false,"given":"Xiaoyong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7336-4003","authenticated-orcid":false,"given":"Jia","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linghui","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4456-7987","authenticated-orcid":false,"given":"Jie","family":"Yuan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4485-6743","authenticated-orcid":false,"given":"Shui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2018.2800740"},{"key":"ref2","volume-title":"CERT Division of the Software Engineering Institute, Price Waterhouse Cooper","year":"2015"},{"key":"ref3","volume-title":"Verizon 2018 Data Breach Investigations Report","year":"2018"},{"key":"ref4","volume-title":"2018 Insider Threat Intelligence Report","year":"2018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM.2016.7795339"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.5220\/0005480407090720"},{"key":"ref7","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. ICLR","author":"Kipf"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/sp.2018.00016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3105761"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/THS.2015.7446229"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3303771"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2014.2327966"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"ref14","article-title":"Deep learning for unsupervised insider threat detection in structured cybersecurity data streams","author":"Tuor","year":"2017","journal-title":"arXiv:1710.00811"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005589"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243811"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/3898951"},{"key":"ref18","article-title":"Eigen co-occurrence matrix method for masquerade detection","volume-title":"Proc. 7th JSSST SIGSYS Workshop Syst. Program. Appl. (SPA)","author":"Oka"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM47813.2019.9020760"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3363224"},{"key":"ref21","first-page":"487","article-title":"SLEUTH: Real-time attack scenario reconstruction from COTS audit data","volume-title":"Proc. USENIX Secur. Symp.","author":"Hossain"},{"key":"ref22","first-page":"1111","article-title":"MPI: Multiple perspective attack investigation with semantics aware execution partitioning","volume-title":"Proc. USENIX Secur. Symp.","author":"Ma"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243763"},{"key":"ref24","first-page":"1","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","volume-title":"Proc. ICLR","author":"Chen"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"ref26","first-page":"5241","article-title":"GMNN: Graph Markov neural networks","volume-title":"Proc. ICML","author":"Qu"},{"key":"ref27","first-page":"1","article-title":"Spectral networks and locally connected networks on graphs","volume-title":"Proc. ICLR","author":"Bruna"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref29","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. NeurIPS","author":"Hamilton"},{"key":"ref30","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. ICLR","author":"Velickovic"},{"key":"ref31","first-page":"1","article-title":"Geom-GCN: Geometric graph convolutional networks","volume-title":"Proc. ICLR","author":"Pei"},{"key":"ref32","first-page":"1","article-title":"Graph wavelet neural network","volume-title":"Proc. ICLR","author":"Xu"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1801.07606"},{"key":"ref35","article-title":"Revisiting graph neural networks: All we have is low-pass filters","author":"Hoang","year":"2019","journal-title":"arXiv:1905.09550"},{"key":"ref36","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"Proc. ICML","author":"Wu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2978386"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5950"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/spw.2013.37"},{"key":"ref41","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. NeurIPS","author":"Paszke"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5521-0"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/csr51186.2021.9527925"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10044727.pdf?arnumber=10044727","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T15:12:41Z","timestamp":1707837161000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10044727\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3245413","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}