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Many AI-based approaches have been proposed to solve the software vulnerability detection (SVD) problem to ensure the security and integrity of software applications (in both the development and testing phases). However, there are still two open and significant issues for SVD in terms of (i) learning automatic representations to improve the predictive performance of SVD, and (ii) tackling the scarcity of labeled vulnerability datasets that conventionally need laborious labeling effort by experts. In this paper, we propose a novel approach to tackle these two crucial issues. We first exploit the automatic representation learning with deep domain adaptation for SVD. We then propose a novel cross-domain kernel classifier leveraging the max-margin principle to significantly improve the transfer learning process of SVs from imbalanced labeled into imbalanced unlabeled projects.\n            <jats:italic>Our approach is the first work that leverages solid body theories of the max-margin principle, kernel methods, and bridging the gap between source and target domains for imbalanced domain adaptation (DA) applied in cross-project SVD<\/jats:italic>\n            . The experimental results on real-world software datasets show the superiority of our proposed method over state-of-the-art baselines. In short, our method obtains a higher performance on F1-measure, one of the most important measures in SVD, from 1.83% to 6.25% compared to the second highest method in the used datasets.\n          <\/jats:p>","DOI":"10.1145\/3664602","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T08:46:47Z","timestamp":1715244407000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Domain Adaptation With Max-Margin Principle for Cross-Project Imbalanced Software Vulnerability Detection"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5838-3409","authenticated-orcid":false,"given":"Van","family":"Nguyen","sequence":"first","affiliation":[{"name":"Software Systems and Cybersecurity, Monash University, Clayton, Australia"},{"name":"Human-Centric Security, CSIRO's Data61, Clayton, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0414-9067","authenticated-orcid":false,"given":"Trung","family":"Le","sequence":"additional","affiliation":[{"name":"Data Science and AI, Monash University, Clayton, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5516-9984","authenticated-orcid":false,"given":"Chakkrit","family":"Tantithamthavorn","sequence":"additional","affiliation":[{"name":"Software Systems and Cybersecurity, Monash University, Clayton, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4928-7076","authenticated-orcid":false,"given":"John","family":"Grundy","sequence":"additional","affiliation":[{"name":"Software Systems and Cybersecurity, Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9977-8247","authenticated-orcid":false,"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[{"name":"Data Science and AI, Monash University, Clayton, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"e_1_3_2_2_1","unstructured":"Mart\u00edn Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry Moore Derek G. 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International Joint Conference on Neural Networks (IJCNN) (2014).","journal-title":"International Joint Conference on Neural Networks (IJCNN)"},{"key":"e_1_3_2_57_1","article-title":"An information-theoretic and contrastive learning-based approach for identifying code statements causing software vulnerability","volume":"2209","author":"Nguyen Van","year":"2022","unstructured":"Van Nguyen, Trung Le, Chakkrit Tantithamthavorn, John Grundy, Hung Nguyen, Seyit Camtepe, Paul Quirk, and Dinh Phung. 2022. An information-theoretic and contrastive learning-based approach for identifying code statements causing software vulnerability. CoRR abs\/2209.10414 (2022).","journal-title":"CoRR"},{"key":"e_1_3_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533907"},{"key":"e_1_3_2_59_1","article-title":"ReGVD: Revisiting graph neural networks for vulnerability detection","author":"Nguyen Van-Anh","year":"2022","unstructured":"Van-Anh Nguyen, Dai Quoc Nguyen, Van Nguyen, Trung Le, Quan Hung Tran, and Dinh Q. Phung. 2022. ReGVD: Revisiting graph neural networks for vulnerability detection. International Conference on Software Engineering (ICSE) (2022).","journal-title":"International Conference on Software Engineering (ICSE)"},{"key":"e_1_3_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"e_1_3_2_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3144348"},{"key":"e_1_3_2_62_1","volume-title":"Advances in Neural Information Processing Systems","author":"Rahimi Ali","year":"2008","unstructured":"Ali Rahimi and Benjamin Recht. 2008. 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CoRR abs\/1807.04320 (2018).","journal-title":"CoRR"},{"key":"e_1_3_2_65_1","article-title":"Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition","volume":"1402","author":"Sak Hasim","year":"2014","unstructured":"Hasim Sak, Andrew W. Senior, and Fran\u00e7oise Beaufays. 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. CoRR abs\/1402.1128 (2014).","journal-title":"CoRR"},{"key":"e_1_3_2_66_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264965"},{"key":"e_1_3_2_67_1","article-title":"Learning with kernels","author":"Sch\u00f6lkopf Bernhard","year":"2002","unstructured":"Bernhard Sch\u00f6lkopf and Alexander J. Smola. 2002. Learning with kernels. The MIT Press (2002).","journal-title":"The MIT Press"},{"key":"e_1_3_2_68_1","article-title":"Neural computation","author":"Sch\u00f6lkopf Bernhard","year":"2000","unstructured":"Bernhard Sch\u00f6lkopf, Alex J. Smola, Robert C. Williamson, and Peter L. Bartlett. 2000. Neural computation. Machine Learning.","journal-title":"Machine Learning"},{"key":"e_1_3_2_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2010.81"},{"key":"e_1_3_2_70_1","volume-title":"International Conference on Learning Representations","author":"Shu Rui","year":"2018","unstructured":"Rui Shu, Hung H. Bui, Hirokazu Narui, and Stefano Ermon. 2018. A DIRT-T approach to unsupervised domain adaptation. In International Conference on Learning Representations."},{"key":"e_1_3_2_71_1","first-page":"3104","volume-title":"Advances in Neural Information Processing Systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems. 3104\u20133112."},{"key":"e_1_3_2_72_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"e_1_3_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510067"},{"key":"e_1_3_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273611"},{"key":"e_1_3_2_75_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1058114"},{"key":"e_1_3_2_76_1","article-title":"Pattern recognition using generalized portrait method","volume":"24","author":"Vapnik Vladimir N.","year":"1963","unstructured":"Vladimir N. Vapnik and Alexander Y. Lerner. 1963. Pattern recognition using generalized portrait method. Automation and Remote Control 24.","journal-title":"Automation and Remote Control"},{"key":"e_1_3_2_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2020.3023177"},{"key":"e_1_3_2_78_1","article-title":"Learning to find pre-images","volume":"16","author":"Weston Jason","year":"2003","unstructured":"Jason Weston, Bernhard Sch\u00f6lkopf, and G\u00f6khan Bakir. 2003. Learning to find pre-images. Advances in Neural Information Processing Systems 16.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_79_1","first-page":"13","volume-title":"Proceedings of the 5th USENIX Conference on Offensive Technologies","author":"Yamaguchi Fabian","year":"2011","unstructured":"Fabian Yamaguchi, Felix Lindner, and Konrad Rieck. 2011. Vulnerability extrapolation: Assisted discovery of vulnerabilities using machine learning. 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