{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T09:24:05Z","timestamp":1753521845210,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442155"},{"type":"electronic","value":"9783031442162"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44216-2_20","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:02:58Z","timestamp":1695279778000},"page":"241-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Graph Neural Network-Based Smart Contract Vulnerability Detection Method with\u00a0Artificial Rule"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5932-9209","authenticated-orcid":false,"given":"Ziyue","family":"Wei","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3668-3600","authenticated-orcid":false,"given":"Weining","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-0716","authenticated-orcid":false,"given":"Xiaohong","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6818-5118","authenticated-orcid":false,"given":"Wenxin","family":"Tao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2958-8066","authenticated-orcid":false,"given":"Tiantian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"issue":"4","key":"20_CR1","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1504\/IJWGS.2018.095647","volume":"14","author":"Z Zheng","year":"2018","unstructured":"Zheng, Z., Xie, S., Dai, H.N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352\u2013375 (2018)","journal-title":"Int. J. Web Grid Serv."},{"key":"20_CR2","unstructured":"Yuan, Z., Zhenguang, L., Peng, Q., Qi, L., Xiang, W., Qinming, H.: Smart contract vulnerability detection using graph neural networks. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI (2020)"},{"key":"20_CR3","unstructured":"Tann, W.J.W., Han, X.J., Gupta, S.S., Ong, Y.S.: Towards safer smart contracts: a sequence learning approach to detecting security threats. arXiv: 1811.06632 (2018)"},{"issue":"2","key":"20_CR4","first-page":"1296","volume":"35","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Qian, P., Wang, X., Zhuang, Y., Qiu, L., Wang, X.: Combining graph neural networks with expert knowledge for smart contract vulnerability detection. IEEE Trans. Knowl. Data Eng. 35(2), 1296\u20131310 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"20_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-319-89722-6_10","volume-title":"Principles of Security and Trust","author":"I Grishchenko","year":"2018","unstructured":"Grishchenko, I., Maffei, M., Schneidewind, C.: A semantic framework for the security analysis of ethereum smart contracts. In: Bauer, L., K\u00fcsters, R. (eds.) POST 2018. LNCS, vol. 10804, pp. 243\u2013269. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-89722-6_10"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Hildenbrandt, E., Saxena, M., Rodrigues, N., et al.: KEVM: a complete formal semantics of the ethereum virtual machine. In: Proceedings of the IEEE 31st Computer Security Foundations Symposium (CSF), pp. 204\u2013217. IEEE (2018)","DOI":"10.1109\/CSF.2018.00022"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Amani, S., B\u00e9gel, M., Bortin, M., Staples, M.: Towards verifying ethereum smart contract bytecode in Isabelle\/HOL. In: Proceedings of the 7th ACM SIGPLAN International Conference on Certified Programs and Proofs, pp. 66\u201377 (2018 )","DOI":"10.1145\/3167084"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Luu, L., Chu, D.H., Olickel, H., Saxena, P., Hobor, A.: Making smart contracts smarter. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 254\u2013269 (2016)","DOI":"10.1145\/2976749.2978309"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Tsankov, P., Dan, A., Drachsler-Cohen, D., Gervais, A., Buenzli, F., Vechev, M.: Securify: practical security analysis of smart contracts. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 67\u201382 (2018)","DOI":"10.1145\/3243734.3243780"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, B., Liu, Y., Chan, W.K.: ContractFuzzer: fuzzing smart contracts for vulnerability detection. In: Proceedings of the 33rd ACM\/IEEE International Conference on Automated Software Engineering, pp. 259\u2013269 (2018)","DOI":"10.1145\/3238147.3238177"},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"43806","DOI":"10.1109\/ACCESS.2020.2977362","volume":"8","author":"G Tian","year":"2020","unstructured":"Tian, G., Wang, Q., Zhao, Y., Guo, L., Sun, Z., Lv, L.: Smart contract classification with a Bi-LSTM based approach. IEEE Access 8, 43806\u201343816 (2020)","journal-title":"IEEE Access"},{"key":"20_CR12","unstructured":"Allamanis, M., Brockschmidt, M., Khademi, M.: Learning to represent programs with graphs. In: International Conference on Learning Representations (2018)"},{"key":"20_CR13","unstructured":"Josselin, F., Grieco, G., Groce, A.: Slither: a static analysis framework for smart contracts. In: 2019 IEEE\/ACM 2nd International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB). IEEE (2019)"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Cheng, H.T., Koc, L., Harmsen, J., et al.: Wide & deep learning for recommender systems[J]. ACM (2016)","DOI":"10.1145\/2988450.2988454"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Feng, Z., et al.: CodeBERT: a pretrained model for programming and natural languages (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"6303","DOI":"10.1109\/TSP.2020.3033962","volume":"68","author":"L Ruiz","year":"2020","unstructured":"Ruiz, L., Gama, F., Ribeiro, A.: Gated graph recurrent neural networks. IEEE Trans. Signal Process. 68, 6303\u20136318 (2020)","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44216-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:06:12Z","timestamp":1695279972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44216-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442155","9783031442162"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44216-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}