{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T13:19:26Z","timestamp":1775135966134,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819584161","type":"print"},{"value":"9789819584178","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-8417-8_15","type":"book-chapter","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:30:44Z","timestamp":1775133044000},"page":"200-215","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MH-GAT: A Buffer Overflow Vulnerability Detection Method via\u00a0Cross-Graph Semantic Alignment"],"prefix":"10.1007","author":[{"given":"Qunlong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peixuan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenrui","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"15_CR1","unstructured":"CVE. http:\/\/cve.mitre.org\/. Accessed 25 Oct 2023"},{"key":"15_CR2","unstructured":"Twingate. CVE-2023-40031 Report - Details, Severity, & Advisories. https:\/\/www.twingate.com\/blog\/tips\/cve-2023-40031. Accessed 25 Oct 2023"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Wu, F., Wang, J., Liu, J., Wang, W.: Vulnerability detection with deep learning. In: ICCC 2017, pp. 1298\u20131302. IEEE (2017)","DOI":"10.1109\/CompComm.2017.8322752"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Russell, R.L., Kim, L.Y., Hamilton, L.H., et al.: Automated vulnerability detection in source code using deep representation learning. In: ICMLA 2018, pp. 757\u2013762. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"15_CR5","unstructured":"Dam, H.K., Tran, T., Pham, T., et al.: Automatic feature learning for vulnerability prediction. arXiv:1708.02368 (2017)"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Li, Z., Zou, D., Xu, S., et al.: VulDeePecker: a deep learning-based system for vulnerability detection. In: NDSS 2018, pp. 1\u201315. The Internet Society (2018)","DOI":"10.14722\/ndss.2018.23158"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Du, X., Chen, B., Li, Y., et al.: Leopard: identifying vulnerable code for vulnerability assessment through program metrics. In: ICSE 2019, pp. 60\u201371. IEEE\/ACM (2019)","DOI":"10.1109\/ICSE.2019.00024"},{"issue":"6","key":"15_CR8","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/TSE.2010.81","volume":"37","author":"Y Shin","year":"2011","unstructured":"Shin, Y., Meneely, A., Williams, L., Osborne, J.A.: Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities. IEEE Trans. Softw. Eng. 37(6), 772\u2013787 (2011)","journal-title":"IEEE Trans. Softw. Eng."},{"issue":"4","key":"15_CR9","doi-asserted-by":"publisher","first-page":"2244","DOI":"10.1109\/TDSC.2021.3051525","volume":"19","author":"Z Li","year":"2022","unstructured":"Li, Z., Zou, D., Xu, S., et al.: SySeVR: a framework for using deep learning to detect software vulnerabilities. IEEE Trans. Dependable Sec. Comput. 19(4), 2244\u20132258 (2022). https:\/\/doi.org\/10.1109\/TDSC.2021.3051525","journal-title":"IEEE Trans. Dependable Sec. Comput."},{"key":"15_CR10","unstructured":"Li, S., Zheng, R., Zhou, A., Liu, L.: A machine learning-based method for detecting buffer overflow attack with high accuracy. In: CNCI 2020, pp. 1\u20136. IEEE (2020)"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Piromsopa, K., Enbody, R.J.: Buffer-overflow protection: the theory. In: EIT 2006, pp. 1\u20135. IEEE (2006)","DOI":"10.1109\/EIT.2006.252128"},{"key":"15_CR12","unstructured":"Wheeler, D.A.: FlawFinder. http:\/\/www.dwheeler.com\/flawfinder. Accessed 25 Oct 2023"},{"key":"15_CR13","unstructured":"Rough Auditing Tool for Security (RATS). https:\/\/code.google.com\/archive\/p\/rough-auditing-tool-for-security\/. Accessed 25 Oct 2023"},{"key":"15_CR14","unstructured":"Checkmarx. https:\/\/www.checkmarx.com\/. Accessed 25 Oct 2023"},{"key":"15_CR15","unstructured":"Zhou, Y., Liu, S., Siow, J.K., et al.: Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In: NeurIPS 2019, pp. 10197\u201310207 (2019)"},{"issue":"1","key":"15_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TDSC.2019.2942930","volume":"19","author":"D Zou","year":"2019","unstructured":"Zou, D., Wang, S., Xu, S., et al.: $$\\mu $$VulDeePecker: a deep learning-based system for multiclass vulnerability detection. IEEE Trans. Dependable Sec. Comput. 19(1), 1 (2019)","journal-title":"IEEE Trans. Dependable Sec. Comput."},{"key":"15_CR17","doi-asserted-by":"publisher","unstructured":"Kim, S., Choi, J., Ahmed, M.E., et al.: VulDeBERT: a vulnerability detection system using BERT. In: ISSREW 2022, pp. 69\u201374. IEEE (2022). https:\/\/doi.org\/10.1109\/ISSREW55968.2022.00042","DOI":"10.1109\/ISSREW55968.2022.00042"},{"key":"15_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.107139","volume":"152","author":"W Niu","year":"2020","unstructured":"Niu, W., Zhang, X., Du, X., Zhao, L., et al.: A deep learning based static taint analysis approach for IoT software vulnerability location. Measurement 152, 107139 (2020)","journal-title":"Measurement"},{"key":"15_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102286","volume":"108","author":"H Yan","year":"2021","unstructured":"Yan, H., Luo, S., Pan, L., Zhang, Y.: Han-BSVD: a hierarchical attention network for binary software vulnerability detection. Comput. Secur. 108, 102286 (2021)","journal-title":"Comput. Secur."},{"key":"15_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106576","volume":"136","author":"S Cao","year":"2021","unstructured":"Cao, S., Sun, X., Bo, L., et al.: BGNN4VD: constructing bidirectional graph neural-network for vulnerability detection. Inf. Softw. Technol. 136, 106576 (2021)","journal-title":"Inf. Softw. Technol."},{"key":"15_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106809","volume":"144","author":"L Wartschinski","year":"2022","unstructured":"Wartschinski, L., Noller, Y., Vogel, T., et al.: VUDENC: vulnerability detection with deep learning on a natural codebase for python. Inf. Softw. Technol. 144, 106809 (2022)","journal-title":"Inf. Softw. Technol."},{"key":"15_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102823","volume":"121","author":"W Guo","year":"2022","unstructured":"Guo, W., Fang, Y., Huang, C., et al.: HyVulDect: a hybrid semantic vulnerability mining system based on graph neural network. Comput. Secur. 121, 102823 (2022)","journal-title":"Comput. Secur."},{"key":"15_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2022.111450","volume":"193","author":"S Salimi","year":"2022","unstructured":"Salimi, S., Kharrazi, M.: VulSlicer: vulnerability detection through code slicing. J. Syst. Softw. 193, 111450 (2022)","journal-title":"J. Syst. Softw."},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Li, Y., Qiang, W., Li, Z., et al.: DL vulnerability detection method based on inter-procedural semantic optimization. J. Cyber Secur. 9(6), 86\u2013101 (2023). https:\/\/doi.org\/10.11959\/j.issn.2096-109x.2023085","DOI":"10.11959\/j.issn.2096-109x.2023085"},{"issue":"28","key":"15_CR25","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.8292","volume":"36","author":"Y Cao","year":"2024","unstructured":"Cao, Y., Peng, J., Dong, Y.: Vulnerability detection based on transformer and high-quality number embedding. Concurr. Comput. Pract. Exp. 36(28), e8292 (2024). https:\/\/doi.org\/10.1002\/cpe.8292","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"15_CR26","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TIFS.2020.3044773","volume":"16","author":"H Wang","year":"2021","unstructured":"Wang, H., Yue, Y., Meng, G., et al.: Combining graph-based learning with automated data collection for code vulnerability detection. IEEE Trans. Inf. Forensics Secur. 16, 1943\u20131958 (2021). https:\/\/doi.org\/10.1109\/TIFS.2020.3044773","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"15_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107246","volume":"160","author":"Z Zheng","year":"2023","unstructured":"Zheng, Z., Zhang, B., Liu, X., et al.: A multitype software buffer overflow vulnerability prediction method based on a software graph structure and a self-attentive graph neural network. Inf. Softw. Technol. 160, 107246 (2023). https:\/\/doi.org\/10.1016\/j.infsof.2023.107246","journal-title":"Inf. Softw. Technol."}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-8417-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:30:48Z","timestamp":1775133048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-8417-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819584161","9789819584178"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-8417-8_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"3 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-cybermatics.org\/2025\/ica3pp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}