{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:07:56Z","timestamp":1779203276587,"version":"3.51.4"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031535543","type":"print"},{"value":"9783031535550","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\/978-3-031-53555-0_6","type":"book-chapter","created":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T05:02:10Z","timestamp":1707800530000},"page":"53-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Source Code Vulnerability Detection Based on\u00a0Graph Structure Representation and\u00a0Attention Mechanisms"],"prefix":"10.1007","author":[{"given":"Yiran","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kewei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baojiang","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,14]]},"reference":[{"issue":"5","key":"6_CR1","doi-asserted-by":"publisher","first-page":"2469","DOI":"10.1109\/TDSC.2019.2954088","volume":"18","author":"G Lin","year":"2019","unstructured":"Lin, G., et al.: Software vulnerability discovery via learning multi-domain knowledge bases. IEEE Trans. Dependable Secure Comput. 18(5), 2469\u20132485 (2019)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Kim, S., et al.: VUDDY: a scalable approach for vulnerable code clone discovery. In: 2017 IEEE Symposium on Security and Privacy (SP). IEEE (2017)","DOI":"10.1109\/SP.2017.62"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Vuldeepecker: a deep learning-based system for vulnerability detection. arXiv preprint: arXiv:1801.01681 (2018)","DOI":"10.14722\/ndss.2018.23158"},{"issue":"5","key":"6_CR4","first-page":"2224","volume":"18","author":"D Zou","year":"2019","unstructured":"Zou, D., et al.: $$\\mu $$VulDeePecker: a deep learning-based system for multiclass vulnerability detection. IEEE Trans. Dependable Secure Comput. 18(5), 2224\u20132236 (2019)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"6_CR5","unstructured":"Zhou, Y., et al.: Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Pradel, M., Sen, K.: Deepbugs: a learning approach to name-based bug detection. Proc. ACM Program. Lang. 2(OOPSLA ), 1-25 (2018)","DOI":"10.1145\/3276517"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Cherem, S., Princehouse, L., Rugina, R.: Practical memory leak detection using guarded value-flow analysis. In: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation (2007)","DOI":"10.1145\/1250734.1250789"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Fan, G., et al.: SMOKE: scalable path-sensitive memory leak detection for millions of lines of code. In: 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE (2019)","DOI":"10.1109\/ICSE.2019.00025"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Heine, D.L., Lam, M.S.: Static detection of leaks in polymorphic containers. In: Proceedings of the 28th International Conference on Software Engineering (2006)","DOI":"10.1145\/1134285.1134321"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Yamaguchi, F., et al.: Modeling and discovering vulnerabilities with code property graphs. In: 2014 IEEE Symposium on Security and Privacy. IEEE (2014)","DOI":"10.1109\/SP.2014.44"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Mou, L., et al.: Convolutional neural networks over tree structures for programming language processing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1 (2016)","DOI":"10.1609\/aaai.v30i1.10139"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Pham, N.H., et al.: Detection of recurring software vulnerabilities. In: Proceedings of the 25th IEEE\/ACM International Conference on Automated Software Engineering (2010)","DOI":"10.1145\/1858996.1859089"},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TIFS.2020.3044773","volume":"16","author":"H Wang","year":"2020","unstructured":"Wang, H., et al.: Combining graph-based learning with automated data collection for code vulnerability detection. IEEE Trans. Inf. Forensics Secur. 16, 1943\u20131958 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"6_CR14","unstructured":"Xu, K., et al.: How powerful are graph neural networks?. arXiv preprint: arXiv:1810.00826 (2018)"},{"key":"6_CR15","unstructured":"Shervashidze, N., et al.: Weisfeiler-lehman graph kernels. J. Mach. Learn. Res. 12(9) (2011)"},{"key":"6_CR16","unstructured":"NIST. Software Assurance Reference Dataset Project. https:\/\/samate.nist.gov\/SRD\/. Accessed 1 June 2019"},{"key":"6_CR17","unstructured":"National Vulnerability Database (NVD). https:\/\/nvd.nist.gov. Accessed 1 May 2020"},{"key":"6_CR18","unstructured":"Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint: arXiv:1810.04805 (2018)"},{"key":"6_CR19","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint: arXiv:1803.02155 (2018)","DOI":"10.18653\/v1\/N18-2074"},{"key":"6_CR21","unstructured":"Joern (2014). https:\/\/joern.readthedocs.io\/en\/latest\/"},{"key":"6_CR22","doi-asserted-by":"crossref","unstructured":"Lipp, S., Banescu, S., Pretschner, A.: An empirical study on the effectiveness of static C code analyzers for vulnerability detection. In: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (2022)","DOI":"10.1145\/3533767.3534380"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: Vulnerability dataset construction methods applied to vulnerability detection: a survey. In: 2022 52nd Annual IEEE\/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE (2022)","DOI":"10.1109\/DSN-W54100.2022.00032"}],"container-title":["Lecture Notes on Data Engineering and Communications Technologies","Advances in Internet, Data &amp; Web Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-53555-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T05:04:43Z","timestamp":1707800683000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53555-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031535543","9783031535550"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53555-0_6","relation":{},"ISSN":["2367-4512","2367-4520"],"issn-type":[{"value":"2367-4512","type":"print"},{"value":"2367-4520","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EIDWT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Emerging Internet, Data & Web Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Naples","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 February 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eidwt12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/eidwt\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}