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Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>\n            Memory-related vulnerabilities can result in performance degradation or even program crashes, constituting severe threats to the security of modern software. Despite the promising results of deep learning (DL)-based vulnerability detectors, there exist three main limitations: (1) rich contextual program semantics related to vulnerabilities have not yet been fully modeled; (2) multi-granularity vulnerability features in hierarchical code structure are still hard to be captured; and (3) heterogeneous flow information is not well utilized. To address these limitations, in this article, we propose a novel DL-based approach, called\n            <jats:italic>MVD+<\/jats:italic>\n            , to detect memory-related vulnerabilities at the statement-level. Specifically, it conducts both intraprocedural and interprocedural analysis to model vulnerability features, and adopts a hierarchical representation learning strategy, which performs syntax-aware neural embedding within statements and captures structured context information across statements based on a novel Flow-Sensitive Graph Neural Networks, to learn both syntactic and semantic features of vulnerable code. To demonstrate the performance, we conducted extensive experiments against eight state-of-the-art DL-based approaches as well as five well-known static analyzers on our constructed dataset with 6,879 vulnerabilities in 12 popular C\/C++ applications. The experimental results confirmed that\n            <jats:italic>MVD+<\/jats:italic>\n            can significantly outperform current state-of-the-art baselines and make a great trade-off between effectiveness and efficiency.\n          <\/jats:p>","DOI":"10.1145\/3624744","type":"journal-article","created":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T11:55:57Z","timestamp":1695038157000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Learning to Detect Memory-related Vulnerabilities"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3688-4437","authenticated-orcid":false,"given":"Sicong","family":"Cao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5165-5080","authenticated-orcid":false,"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7267-4923","authenticated-orcid":false,"given":"Lili","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4648-3795","authenticated-orcid":false,"given":"Rongxin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8500-9917","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5432-651X","authenticated-orcid":false,"given":"Xiaoxue","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0698-7307","authenticated-orcid":false,"given":"Chuanqi","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology\/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6272-4069","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Macau University of Science and Technology, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8503-4063","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Yangzhou University, China"}]}],"member":"320","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR\u201918)","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. 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