{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T07:05:02Z","timestamp":1784617502807,"version":"3.55.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Higher Education Research Project of Heilongjiang Higher Education Association","award":["No.23GJYBB080"],"award-info":[{"award-number":["No.23GJYBB080"]}]},{"name":"Higher Education Teaching Reform Research Key Commissioned Project of Heilongjiang Province","award":["No.SJGZ20220086"],"award-info":[{"award-number":["No.SJGZ20220086"]}]},{"name":"Industry-Academia Collaborative Education Project of Ministry of Education","award":["No.2310047233314013"],"award-info":[{"award-number":["No.2310047233314013"]}]},{"name":"Supported by Intelligent Policing Key Laboratory of Sichuan Province","award":["No.ZNJW2025KFMS009"],"award-info":[{"award-number":["No.ZNJW2025KFMS009"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 61402126"],"award-info":[{"award-number":["No. 61402126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-026-08724-x","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:48:14Z","timestamp":1784616494000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heap overflow vulnerability detection based on deep learning and multimodal fusion"],"prefix":"10.1007","volume":"82","author":[{"given":"Jiqiang","family":"Zhai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"8724_CR1","first-page":"503","volume":"86","author":"LJ Trautman","year":"2018","unstructured":"Trautman LJ, Ormerod PC (2018) Wannacry, ransomware, and the emerging threat to corporations. Tenn L Rev 86:503","journal-title":"Tenn L Rev"},{"key":"8724_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102860","volume":"121","author":"AO Almashhadani","year":"2022","unstructured":"Almashhadani AO, Carlin D, Kaiiali M, Sezer S (2022) MFMCNS: a multi-feature and multi-classifier network-based system for ransomworm detection. Comput Secur 121:102860","journal-title":"Comput Secur"},{"key":"8724_CR3","unstructured":"Heelan S, Melham T, Kroening D (2018) Automatic heap layout manipulation for exploitation. In: 27th USENIX Security Symposium (USENIX Security 18), pp 763\u2013779"},{"key":"8724_CR4","doi-asserted-by":"crossref","unstructured":"Veen V, Dutt-Sharma N, Cavallaro L, Bos H (2012) Memory errors: the past, the present, and the future. In: Research in Attacks, Intrusions, and Defenses: 15th International Symposium, RAID 2012, Amsterdam, The Netherlands, September 12\u201314, 2012. Proceedings 15. Springer, pp 86\u2013106","DOI":"10.1007\/978-3-642-33338-5_5"},{"issue":"1","key":"8724_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s42400-024-00244-6","volume":"7","author":"Q Shen","year":"2024","unstructured":"Shen Q, Meng G, Chen K (2024) Revealing the exploitability of heap overflow through PoC analysis. Cybersecurity 7(1):47","journal-title":"Cybersecurity"},{"issue":"OOPSLA2","key":"8724_CR6","doi-asserted-by":"publisher","first-page":"1615","DOI":"10.1145\/3689768","volume":"8","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Liu T, Wang Y, Qi Y, Ji K, Tang J, Wang X, Li X, Zuo Z (2024) Hardtaint: production-run dynamic taint analysis via selective hardware tracing. Proc ACM Program Lang 8(OOPSLA2):1615\u20131640","journal-title":"Proc ACM Program Lang"},{"issue":"3","key":"8724_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3182657","volume":"51","author":"R Baldoni","year":"2018","unstructured":"Baldoni R, Coppa E, D\u2019elia DC, Demetrescu C, Finocchi I (2018) A survey of symbolic execution techniques. ACM Comput Surv (CSUR) 51(3):1\u201339","journal-title":"ACM Comput Surv (CSUR)"},{"key":"8724_CR8","doi-asserted-by":"crossref","unstructured":"Chowdhary K, Chowdhary K (2020) Natural language processing. In: Fundamentals of Artificial Intelligence, pp 603\u2013649","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"8724_CR9","doi-asserted-by":"publisher","first-page":"172231","DOI":"10.1109\/ACCESS.2019.2956508","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao L, Zhao J (2019) A survey on the new generation of deep learning in image processing. IEEE Access 7:172231\u2013172263","journal-title":"IEEE Access"},{"key":"8724_CR10","doi-asserted-by":"crossref","unstructured":"Wu J, He J (2024) Trustworthy transfer learning: a survey. arXiv preprint arXiv:2412.14116","DOI":"10.1613\/jair.1.17602"},{"issue":"10","key":"8724_CR11","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s10462-024-10825-z","volume":"57","author":"M Lymperaiou","year":"2024","unstructured":"Lymperaiou M, Stamou G (2024) A survey on knowledge-enhanced multimodal learning. Artif Intell Rev 57(10):284","journal-title":"Artif Intell Rev"},{"key":"8724_CR12","doi-asserted-by":"crossref","unstructured":"Gupta J, Pathak S, Kumar G (2022) Deep learning (CNN) and transfer learning: a review. In: Journal of Physics: Conference Series, vol 2273. IOP Publishing, p 012029","DOI":"10.1088\/1742-6596\/2273\/1\/012029"},{"key":"8724_CR13","doi-asserted-by":"crossref","unstructured":"Siami-Namini S, Tavakoli N, Namin AS (2019) The performance of LSTM and BiLSTM in forecasting time series. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE, pp 3285\u20133292","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"8724_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.neucom.2021.04.038","volume":"454","author":"J Li","year":"2021","unstructured":"Li J, Wang X, Tu Z, Lyu MR (2021) On the diversity of multi-head attention. Neurocomputing 454:14\u201324","journal-title":"Neurocomputing"},{"issue":"1","key":"8724_CR15","first-page":"1","volume":"11","author":"AR Khaliq","year":"2023","unstructured":"Khaliq AR, Ullah S, Ahmad T, Yadav A, Majid MI (2023) Behavioral analysis of backdoor malware exploiting heap overflow vulnerabilities using data mining and machine learning. Pak J Eng Technol Sci 11(1):1\u201313","journal-title":"Pak J Eng Technol Sci"},{"key":"8724_CR16","doi-asserted-by":"crossref","unstructured":"Cotroneo D, Grasso FC, Natella R, Orbinato V (2025) Can neural decompilation assist vulnerability prediction on binary code? In: Proceedings of the 18th European Workshop on Systems Security, pp 26\u201332","DOI":"10.1145\/3722041.3723097"},{"key":"8724_CR17","doi-asserted-by":"crossref","unstructured":"Mouzarani M, Sadeghiyan B, Zolfaghari M (2015) A smart fuzzing method for detecting heap-based buffer overflow in executable codes. In: IEEE 21st Pacific Rim International Symposium on Dependable Computing (PRDC). IEEE, pp 42\u201349","DOI":"10.1109\/PRDC.2015.10"},{"key":"8724_CR18","doi-asserted-by":"crossref","unstructured":"Shudrak M (2017) Winheap explorer: Efficient and transparent heap-based bug detection in machine code. In: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, pp 94\u2013101","DOI":"10.1109\/QRS.2017.20"},{"key":"8724_CR19","doi-asserted-by":"crossref","unstructured":"Liu H, Silvestro S, Wang X, Duan L, Liu T (2019) Csod: context-sensitive overflow detection. In: 2019 IEEE\/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, pp 50\u201360","DOI":"10.1109\/CGO.2019.8661198"},{"issue":"5","key":"8724_CR20","first-page":"2224","volume":"18","author":"D Zou","year":"2019","unstructured":"Zou D, Wang S, Xu S, Li Z, Jin H (2019) Vuldeepecker: a deep learning-based system for multiclass vulnerability detection. IEEE Trans Dependable Secure Comput 18(5):2224\u20132236","journal-title":"IEEE Trans Dependable Secure Comput"},{"issue":"19","key":"8724_CR21","doi-asserted-by":"publisher","first-page":"4086","DOI":"10.3390\/app9194086","volume":"9","author":"Y Lee","year":"2019","unstructured":"Lee Y, Kwon H, Choi S-H, Lim S-H, Baek SH, Park K-W (2019) Instruction2vec: efficient preprocessor of assembly code to detect software weakness with CNN. Appl Sci 9(19):4086","journal-title":"Appl Sci"},{"issue":"2","key":"8724_CR22","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1002\/spy2.100","volume":"3","author":"EH Boudjema","year":"2020","unstructured":"Boudjema EH, Verlan S, Mokdad L, Faure C (2020) Vyper: vulnerability detection in binary code. Secur Priv 3(2):100","journal-title":"Secur Priv"},{"key":"8724_CR23","doi-asserted-by":"crossref","unstructured":"Liu J, An H, Li J, Liang H (2022) Detecting exploit primitives automatically for heap vulnerabilities on binary programs. arXiv preprint arXiv:2212.13990","DOI":"10.1145\/3573428.3573550"},{"key":"8724_CR24","unstructured":"Thomasson E, Widesk\u00e4r L (2023) Amplifying heap overflow vulnerability detection with reinforcement learning"},{"key":"8724_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107246","volume":"160","author":"Z Zheng","year":"2023","unstructured":"Zheng Z, Liu Y, Zhang B, Liu X, He H, Gong X (2023) 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","journal-title":"Inf Softw Technol"},{"key":"8724_CR26","unstructured":"Lin Z, Yu Z, Guo Z, Campanoni S, Dinda P, Xing X (2024) $$\\{$$CAMP$$\\}$$: compiler and allocator-based heap memory protection. In: 33rd USENIX Security Symposium (USENIX Security 24), pp 4015\u20134032"},{"issue":"5","key":"8724_CR27","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.3390\/s24051351","volume":"24","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Hu Y, Chen X (2024) Context and multi-features-based vulnerability detection: a vulnerability detection frame based on context slicing and multi-features. Sensors 24(5):1351","journal-title":"Sensors"},{"key":"8724_CR28","doi-asserted-by":"crossref","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (long and Short Papers), pp 4171\u20134186","DOI":"10.18653\/v1\/N19-1423"},{"key":"8724_CR29","first-page":"358","volume":"357","author":"N Privault","year":"2013","unstructured":"Privault N (2013) Understanding Markov chains: examples and applications. Springer Singapore 357:358","journal-title":"Springer Singapore"},{"key":"8724_CR30","doi-asserted-by":"crossref","unstructured":"Tao J, Gu Y, Sun J, Bie Y, Wang H (2021) Research on vgg16 convolutional neural network feature classification algorithm based on transfer learning. In: 2021 2nd China International SAR Symposium (CISS). IEEE, pp 1\u20133","DOI":"10.23919\/CISS51089.2021.9652277"},{"key":"8724_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, vol 30"},{"key":"8724_CR32","doi-asserted-by":"crossref","unstructured":"Bai Y (2022) Relu-function and derived function review. In: SHS Web of Conferences, vol 144. EDP Sciences, p 02006","DOI":"10.1051\/shsconf\/202214402006"},{"issue":"2","key":"8724_CR33","first-page":"1009","volume":"24","author":"B Jabir","year":"2021","unstructured":"Jabir B, Falih N (2021) Dropout, a basic and effective regularization method for a deep learning model: a case study. Indones J Electr Eng Comput Sci 24(2):1009\u20131016","journal-title":"Indones J Electr Eng Comput Sci"},{"issue":"23","key":"8724_CR34","doi-asserted-by":"publisher","first-page":"4823","DOI":"10.3390\/math11234823","volume":"11","author":"J Li","year":"2023","unstructured":"Li J, Lu G, Gao Y, Gao F (2023) A smart contract vulnerability detection method based on multimodal feature fusion and deep learning. Mathematics 11(23):4823","journal-title":"Mathematics"},{"issue":"5","key":"8724_CR35","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.3390\/s23052381","volume":"23","author":"M Paw\u0142owski","year":"2023","unstructured":"Paw\u0142owski M, Wr\u00f3blewska A, Sysko-Roma\u0144czuk S (2023) Effective techniques for multimodal data fusion: a comparative analysis. Sensors 23(5):2381","journal-title":"Sensors"},{"key":"8724_CR36","doi-asserted-by":"crossref","unstructured":"Li J, Yang S, Wu S, Shi H, Zheng C, Xu H, Jia J (2025) Logits-based finetuning. arXiv preprint arXiv:2505.24461","DOI":"10.18653\/v1\/2025.emnlp-main.745"},{"key":"8724_CR37","doi-asserted-by":"crossref","unstructured":"Pratiwi H, Windarto AP, Susliansyah S, Aria RR, Susilowati S, Rahayu LK, Fitriani Y, Merdekawati A, Rahadjeng IR (2020) Sigmoid activation function in selecting the best model of artificial neural networks. In: Journal of Physics: Conference Series, vol 1471. IOP Publishing, p 012010","DOI":"10.1088\/1742-6596\/1471\/1\/012010"},{"key":"8724_CR38","unstructured":"NSA Center for Assured Software: SARD (2017) Juliet 1.3 test suite. https:\/\/samate.nist.gov\/SARD\/test-suites\/112"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08724-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-026-08724-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-026-08724-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:48:19Z","timestamp":1784616499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-026-08724-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["8724"],"URL":"https:\/\/doi.org\/10.1007\/s11227-026-08724-x","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]},"assertion":[{"value":"17 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"582"}}