{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:12:37Z","timestamp":1769746357738,"version":"3.49.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030415785","type":"print"},{"value":"9783030415792","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-41579-2_13","type":"book-chapter","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T16:09:09Z","timestamp":1581955749000},"page":"219-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Deep Learning-Based Vulnerable Function Detection: A Benchmark"],"prefix":"10.1007","author":[{"given":"Guanjun","family":"Lin","sequence":"first","affiliation":[]},{"given":"Wei","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Xiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,18]]},"reference":[{"key":"13_CR1","unstructured":"Software assurance reference dataset project. https:\/\/samate.nist.gov\/SRD\/ (2019). Accessed: 20 Aug 2019"},{"key":"13_CR2","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265\u2013283 (2016)"},{"issue":"4","key":"13_CR3","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1145\/3212695","volume":"51","author":"M Allamanis","year":"2018","unstructured":"Allamanis, M., Barr, E.T., Devanbu, P., Sutton, C.: A survey of machine learning for big code and naturalness. ACM Comput. Surv. (CSUR) 51(4), 81 (2018)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Black, P.E., Black, P.E.: Juliet 1.3 Test Suite: Changes From 1.2. US Department of Commerce, National Institute of Standards and Technology (2018)","DOI":"10.6028\/NIST.TN.1995"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)","DOI":"10.1162\/tacl_a_00051"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Choi, M.J., Jeong, S., Oh, H., Choo, J.: End-to-end prediction of buffer overruns from raw source code via neural memory networks. arXiv preprint arXiv:1703.02458 (2017)","DOI":"10.24963\/ijcai.2017\/214"},{"key":"13_CR7","unstructured":"Chollet, F., et al.: Keras. https:\/\/github.com\/fchollet\/keras (2015)"},{"key":"13_CR8","volume-title":"Introduction to Information Retrieval","author":"CD Manning","year":"2009","unstructured":"Manning, C.D., Raghavan, P., Sch\u00fctze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2009)"},{"issue":"3","key":"13_CR9","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.1007\/s11277-017-5069-3","volume":"102","author":"F Dong","year":"2018","unstructured":"Dong, F., Wang, J., Li, Q., Xu, G., Zhang, S.: Defect prediction in android binary executables using deep neural network. Wireless Pers. Commun. 102(3), 2261\u20132285 (2018)","journal-title":"Wireless Pers. Commun."},{"key":"13_CR10","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 Conference on Data and Application Security and Privacy, pp. 85\u201396. ACM (2016)","DOI":"10.1145\/2857705.2857720"},{"key":"13_CR11","unstructured":"Harer, J.A., et al.: Automated software vulnerability detection with machine learning. arXiv preprint arXiv:1803.04497 (2018)"},{"issue":"8","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"13_CR14","unstructured":"Kostadinov, S.: Understanding GRU networks, December 2017. https:\/\/www.towardsdatascience.com. Accessed 30 Apr 2019"},{"key":"13_CR15","unstructured":"Le, T., et al.: Maximal divergence sequential autoencoder for binary software vulnerability detection. In: Proceedings of the 7th International Conference on Learning Representations (2018)"},{"key":"13_CR16","unstructured":"Lee, Y.J., Choi, S.H., Kim, C., Lim, S.H., Park, K.W.: Learning binary code with deep learning to detect software weakness. In: KSII The 9th International Conference on Internet (ICONI) 2017 Symposium (2017)"},{"key":"13_CR17","unstructured":"Li, Z., et al.: SySeVR: A framework for using deep learning to detect software vulnerabilities. arXiv preprint arXiv:1807.06756 (2018)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Li, Z., et al.: VulDeePecker: a deep learning-based system for vulnerability detection. In: Proceedings of NDSS (2018)","DOI":"10.14722\/ndss.2018.23158"},{"key":"13_CR19","doi-asserted-by":"publisher","unstructured":"Lin, G., et al.: Software vulnerability discovery via learning multi-domain knowledge bases. IEEE Transactions on Dependable and Secure Computing (2019). https:\/\/doi.org\/10.1109\/TDSC.2019.2954088","DOI":"10.1109\/TDSC.2019.2954088"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Lin, G., Zhang, J., Luo, W., Pan, L., Xiang, Y.: POSTER: vulnerability discovery with function representation learning from unlabeled projects. In: Proceedings of the 2017 SIGSAC Conference on CCS, pp. 2539\u20132541. ACM (2017)","DOI":"10.1145\/3133956.3138840"},{"issue":"7","key":"13_CR21","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1109\/TII.2018.2821768","volume":"14","author":"G Lin","year":"2018","unstructured":"Lin, G., et al.: Cross-project transfer representation learning for vulnerable function discovery. IEEE Trans. Ind. Inform. 14(7), 3289\u20133297 (2018)","journal-title":"IEEE Trans. Ind. Inform."},{"key":"13_CR22","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"13_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/978-3-319-25159-2_49","volume-title":"Knowledge Science, Engineering and Management","author":"H Peng","year":"2015","unstructured":"Peng, H., Mou, L., Li, G., Liu, Y., Zhang, L., Jin, Z.: Building program vector representations for deep learning. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS (LNAI), vol. 9403, pp. 547\u2013553. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25159-2_49"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"13_CR25","volume-title":"TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning","author":"B Ramsundar","year":"2018","unstructured":"Ramsundar, B., Zadeh, R.B.: TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning. O\u2019Reilly Media, Inc., Newton (2018)"},{"key":"13_CR26","unstructured":"\u0158eh\u016f\u0159ek, R., Sojka, P.: Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta, pp. 45\u201350, May 2010. http:\/\/is.muni.cz\/publication\/884893\/en"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Russell, R., et al.: Automated vulnerability detection in source code using deep representation learning. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 757\u2013762. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"13_CR28","unstructured":"Sestili, C.D., Snavely, W.S., VanHoudnos, N.M.: Towards security defect prediction with AI. arXiv preprint arXiv:1808.09897 (2018)"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Shar, L.K., Tan, H.B.K.: Predicting common web application vulnerabilities from input validation and sanitization code patterns. In: 2012 Proceedings of the 27th IEEE\/ACM International Conference on Automated Software Engineering, pp. 310\u2013313. IEEE (2012)","DOI":"10.1145\/2351676.2351733"},{"key":"13_CR30","unstructured":"Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440\u20132448 (2015)"},{"key":"13_CR31","unstructured":"Weston, J., Chopra, S., Bordes, A.: Memory networks. arXiv preprint arXiv:1410.3916 (2014)"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Wu, F., Wang, J., Liu, J., Wang, W.: Vulnerability detection with deep learning. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 1298\u20131302. IEEE (2017)","DOI":"10.1109\/CompComm.2017.8322752"}],"container-title":["Lecture Notes in Computer Science","Information and Communications Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-41579-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T20:17:31Z","timestamp":1606421851000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-41579-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030415785","9783030415792"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-41579-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"18 February 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information and Communications Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icics2019","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"199","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":"47","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":"24% - 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":"8","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)"}}]}}