{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:26:40Z","timestamp":1743157600126,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819991181"},{"type":"electronic","value":"9789819991198"}],"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\/978-981-99-9119-8_11","type":"book-chapter","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T13:03:04Z","timestamp":1706878984000},"page":"113-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Software Vulnerabilities Based on\u00a0Hierarchical Graph Attention Network"],"prefix":"10.1007","author":[{"given":"Wenlin","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinsong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,3]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Hin, D., Kan, A., Chen, H., Babar, M. A.: LineVD: statement-level vulnerability detection using graph neural networks. In: Proceedings of the 19th International Conference on Mining Software Repositories, pp. 596\u2013607. ACM, Pittsburgh, PA, USA (2022)","DOI":"10.1145\/3524842.3527949"},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s13198-020-01036-0","volume":"12","author":"A Gupta","year":"2021","unstructured":"Gupta, A., Suri, B., Kumar, V., Jain, P.: Extracting rules for vulnerabilities detection with static metrics using machine learning. Int. J. Syst. Assur. Eng. Manag. 12(1), 65\u201376 (2021)","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Kronjee, J., Hommersom, A., Vranken, H.: Discovering software vulnerabilities using data-flow analysis and machine learning. In: Proceedings of the 13th International Conference on Availability, Reliability and Security, pp. 6:1\u20136:10. Springer, Hamburg (2018)","DOI":"10.1145\/3230833.3230856"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Grieco, G., Grinblat, G. L., Uzal, L., Rawat, S., Feist, J., Mounier, L.: Toward Large-scale vulnerability discovery using machine learning. In: Proceedings of the Sixth ACM on Conference on Data and Application Security and Privacy, pp. 85\u201396. New Orleans, LA, USA (2016)","DOI":"10.1145\/2857705.2857720"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Liu, H., Lang, B.: Machine learning and deep learning methods for intrusion detection systems: a survey. Appl. Sci. 9(20), 4396 (2019)","DOI":"10.3390\/app9204396"},{"issue":"1","key":"11_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10922-021-09624-6","volume":"30","author":"PR Vishnu","year":"2022","unstructured":"Vishnu, P.R., Vinod, P., Yerima, S.Y.: A deep learning approach for classifying vulnerability descriptions using self attention based neural network. J. Netw. Syst. Manag. 30(1), 1\u201327 (2022)","journal-title":"J. Netw. Syst. Manag."},{"key":"11_CR7","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., Kehrer, T., Grunske, L.: VUDENC: vulnerability detection with deep learning on a natural codebase for Python. Inf. Softw. Technol. 144, 106809 (2022)","journal-title":"Inf. Softw. Technol."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Thapa, C., Jang, S. I., Ahmed, M. E., Camtepe, S., Pieprzyk, J., Nepal, S.: Transformer-based language models for software vulnerability detection. In: Proceedings of the 38th Annual Computer Security Applications Conference, pp. 481\u2013496. Austin, TX, USA (2022)","DOI":"10.1145\/3564625.3567985"},{"key":"11_CR9","unstructured":"Zhou, Y., Liu, S., Siow, J., Du, X., Liu, Y.: Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Zheng, W., Jiang, Y., Su, X.: Vu1SPG: vulnerability detection based on slice property graph representation learning. In: 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), pp. 457\u2013467. IEEE, Vancouver, BC, Canada (2021)","DOI":"10.1109\/ISSRE52982.2021.00054"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Cheng, X., Wang, H., Hua, J., Xu, G., Sui, Y.: Deepwukong: statically detecting software vulnerabilities using deep graph neural network. ACM Trans. Softw. Eng. Methodol. (TOSEM) 30(3) (2021)","DOI":"10.1145\/3436877"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Nguyen, V.A., Nguyen, D.Q., Nguyen, V., Le, T., Tran, Q.H., Phung, D.: ReGVD: revisiting graph neural networks for vulnerability detection. In: Proceedings of the ACM\/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pp. 178\u2013182. ACM\/IEEE, Pittsburgh, PA, USA (2022)","DOI":"10.1145\/3510454.3516865"},{"key":"11_CR13","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, (2018)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Zeng, J., Liu, T., Jia, W., Zhou, J.: Fine-grained question-answer sentiment classification with hierarchical graph attention network. Neurocomputing 457 (2021)","DOI":"10.1016\/j.neucom.2021.06.040"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: Vuldeepecker: a deep learning-based system for vulnerability detection. In: 25th Annual Network and Distributed System Security Symposium (NDSS), San Diego, CA, USA (2018)","DOI":"10.14722\/ndss.2018.23158"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Zou, D., Wang, S., Xu, S., Li, Z., Jin, H.:$$\\mu $$VulDeePecker: a deep learning-based system for multiclass vulnerability detection. IEEE Trans. Depend. Secure Comput. 18(5) (2019)","DOI":"10.1109\/TDSC.2019.2942930"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Hao, Y., Dong, Li., Wei, F., Xu, K.: Visualizing and understanding the effectiveness of BERT. In: EMNLP-IJCNLP 2019, pp. 4141\u20134150. Hong Kong, China (2019)","DOI":"10.18653\/v1\/D19-1424"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Yamaguchi, F., Golde, N., Arp, D., Rieck, K.: Modeling and discovering vulnerabilities with code property graphs. In: 2014 IEEE Symposium on Security and Privacy, pp. 590\u2013604. IEEE, Berkeley, California, USA (2014)","DOI":"10.1109\/SP.2014.44"},{"key":"11_CR19","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations (ICLR), Vancouver, BC, Canada (2018)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Xu. W., Li, T., Wang, J., Tang, Y.: Detecting vulnerable software functions via text and dependency features. Soft Comput. 27(9), (2023)","DOI":"10.1007\/s00500-022-07775-5"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yao, Y., Hu, J., Zhao, Y., Li, S., Hu, J.: Deep autoencoder neural networks for short-term traffic congestion prediction of transportation networks, 19(10) (2019)","DOI":"10.3390\/s19102229"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104. ACM, Dallas, Texas, USA (2000)","DOI":"10.1145\/342009.335388"},{"key":"11_CR23","unstructured":"SARD https:\/\/samate.nist.gov\/SRD\/"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Krishna, R., Ding, Y., Ray, B.: Deep learning based vulnerability detection: are we there yet. IEEE Trans. Softw. Eng. 48(9) (2021)","DOI":"10.1109\/TSE.2021.3087402"},{"key":"11_CR25","unstructured":"NVD https:\/\/nvd.nist.gov\/"},{"key":"11_CR26","unstructured":"FlawFinder https:\/\/dwheeler.com\/flawfinder\/"},{"key":"11_CR27","unstructured":"Rats. https:\/\/code.google.com\/archive\/p\/rough-auditing-tool-for-security\/"},{"key":"11_CR28","unstructured":"Joern. https:\/\/joern.io\/"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9119-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T13:05:14Z","timestamp":1706879114000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9119-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819991181","9789819991198"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9119-8_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fuzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"376","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":"101","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":"16","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":"27% - 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":"2.9","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":"1.9","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)"}}]}}