{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:03:09Z","timestamp":1775228589701,"version":"3.50.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031565793","type":"print"},{"value":"9783031565809","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56580-9_16","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:02:23Z","timestamp":1712034143000},"page":"264-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Persistent Clean-Label Backdoor on\u00a0Graph-Based Semi-supervised Cybercrime Detection"],"prefix":"10.1007","author":[{"given":"Xiao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Gaolei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: Neighboring backdoor attacks on graph convolutional network. CoRR abs\/2201.06202 (2022)","DOI":"10.2139\/ssrn.4406116"},{"key":"16_CR2","unstructured":"Dai, H., et al.: Adversarial attack on graph structured data. In: Proceedings of International Conference on Machine Learning (ICML), vol. 80, pp. 1123\u20131132 (2018)"},{"key":"16_CR3","unstructured":"Feng, W., et al.: Graph random neural networks for semi-supervised learning on graphs. In: Annual Conference on Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"16_CR4","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"16_CR5","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR). OpenReview.net (2017)"},{"key":"16_CR6","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (ICLR) (2018)"},{"issue":"4","key":"16_CR7","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/S0378-8733(00)00031-9","volume":"22","author":"B Ruhnau","year":"2000","unstructured":"Ruhnau, B.: Eigenvector-centrality - a node-centrality? Soc. Netw. 22(4), 357\u2013365 (2000)","journal-title":"Soc. Netw."},{"key":"16_CR8","unstructured":"Thekumparampil, K.K., Wang, C., Oh, S., Li, L.: Attention-based graph neural network for semi-supervised learning. CoRR abs\/1803.03735 (2018). http:\/\/arxiv.org\/abs\/1803.03735"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Verma, V., Qu, M., Kawaguchi, K., Lamb, A., Bengio, Y., Kannala, J., Tang, J.: GraphMix: improved training of GNNs for semi-supervised learning. In: AAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 10024\u201310032 (2021)","DOI":"10.1609\/aaai.v35i11.17203"},{"key":"16_CR10","unstructured":"Xi, Z., Pang, R., Ji, S., Wang, T.: Graph backdoor. In: USENIX Security Symposium (USENIX Security), pp. 1523\u20131540 (2021)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Xu, J., Picek, S.: Poster: clean-label backdoor attack on graph neural networks. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS \u201922, pp. 3491\u20133493 (2022)","DOI":"10.1145\/3548606.3563531"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Xu, J., Xue, M., Picek, S.: Explainability-based backdoor attacks against graph neural networks. In: Proceedings of ACM Workshop on Wireless Security and Machine Learning, pp. 31\u201336 (2021)","DOI":"10.1145\/3468218.3469046"},{"key":"16_CR13","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (ICLR). OpenReview.net (2018)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, J., Luo, Y.: Degree centrality, betweenness centrality, and closeness centrality in social network. In: Proceedings of International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017), pp. 300\u2013303 (2017)","DOI":"10.2991\/msam-17.2017.68"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Digital Forensics and Cyber Crime"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56580-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:08:52Z","timestamp":1712034532000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56580-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031565793","9783031565809"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56580-9_16","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"value":"1867-8211","type":"print"},{"value":"1867-822X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDF2C","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Digital Forensics and Cyber Crime","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdf2c2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}