Multimodal information fusion for software vulnerability detection based on both source and binary codes
•The study detects software vulnerability based on both source and binary codes.•A novel fusion strategy is designed to accommodate the characteristics of two modalities.•ChatGPT and two pre-trained models are used for the process of feature analysis process. Context: Many researchers have proposed...
Uložené v:
| Vydané v: | Science of computer programming Ročník 250; s. 103411 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.05.2026
|
| Predmet: | |
| ISSN: | 0167-6423 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | •The study detects software vulnerability based on both source and binary codes.•A novel fusion strategy is designed to accommodate the characteristics of two modalities.•ChatGPT and two pre-trained models are used for the process of feature analysis process.
Context: Many researchers have proposed vulnerability detection methods to enhance software reliability by analyzing the program. However, some vulnerabilities are difficult to be identified only from the source codes, especially the ones related to the execution.
Objectives: To solve this problem, this paper introduces extra binary codes and proposes a novel solution for software vulnerability detection based on the multimodal information fusion.
Methods: The approach treats the source and binary codes as different modalities, and uses two pre-trained models as feature extractors to analyze them separately. Then, we design an attention-based information fusion strategy that taking the information from source codes as the main body while the one from binary codes as the supplement. It could not only capture the correlations among features across different modalities, but also filter the redundancy from the binary codes in the fusion process. In this way, a more comprehensive representation of software is gained and finally taken as the basis for the vulnerability detection.
Results: Our method was comprehensively evaluated on three widely-used datasets in different languages, that is Reveal in C, Devign in C++, and Code_vulnerability_java in Java: (1) For vulnerability detection performance, the Accuracy reached 86.09 %, 84.58 %, and 80.43 % across the three datasets, with F1-scores of 82.87 %, 84.62 %, and 79.58 % respectively; (2) Compared with seven state-of-the-art baseline methods, our approach achieved Accuracy improvements of 2.38 %-3.01 % and F1-score enhancements of 2.32 %-8.47 % across the datasets; (3) Moreover, the ablation experiment shows when combining binary codes with source codes (versus using source codes alone), the Accuracy improved by 6.83 %-13.76 % and F1-score increased by 5.36 %-9.86 %, demonstrating the significant performance gains from multimodal data integration.
Conclusion: The results show that our approach can achieve good performance for the task of software vulnerability detection. Meanwhile, ablation experiments confirm the contributions of binary codes to the detection and indicate the effectiveness of our fusion strategy. We have released the codes and datasets (https://github.com/Wangqxn/Vul-detection) to facilitate follow-up research. |
|---|---|
| AbstractList | •The study detects software vulnerability based on both source and binary codes.•A novel fusion strategy is designed to accommodate the characteristics of two modalities.•ChatGPT and two pre-trained models are used for the process of feature analysis process.
Context: Many researchers have proposed vulnerability detection methods to enhance software reliability by analyzing the program. However, some vulnerabilities are difficult to be identified only from the source codes, especially the ones related to the execution.
Objectives: To solve this problem, this paper introduces extra binary codes and proposes a novel solution for software vulnerability detection based on the multimodal information fusion.
Methods: The approach treats the source and binary codes as different modalities, and uses two pre-trained models as feature extractors to analyze them separately. Then, we design an attention-based information fusion strategy that taking the information from source codes as the main body while the one from binary codes as the supplement. It could not only capture the correlations among features across different modalities, but also filter the redundancy from the binary codes in the fusion process. In this way, a more comprehensive representation of software is gained and finally taken as the basis for the vulnerability detection.
Results: Our method was comprehensively evaluated on three widely-used datasets in different languages, that is Reveal in C, Devign in C++, and Code_vulnerability_java in Java: (1) For vulnerability detection performance, the Accuracy reached 86.09 %, 84.58 %, and 80.43 % across the three datasets, with F1-scores of 82.87 %, 84.62 %, and 79.58 % respectively; (2) Compared with seven state-of-the-art baseline methods, our approach achieved Accuracy improvements of 2.38 %-3.01 % and F1-score enhancements of 2.32 %-8.47 % across the datasets; (3) Moreover, the ablation experiment shows when combining binary codes with source codes (versus using source codes alone), the Accuracy improved by 6.83 %-13.76 % and F1-score increased by 5.36 %-9.86 %, demonstrating the significant performance gains from multimodal data integration.
Conclusion: The results show that our approach can achieve good performance for the task of software vulnerability detection. Meanwhile, ablation experiments confirm the contributions of binary codes to the detection and indicate the effectiveness of our fusion strategy. We have released the codes and datasets (https://github.com/Wangqxn/Vul-detection) to facilitate follow-up research. |
| ArticleNumber | 103411 |
| Author | Jiang, Shuang Wang, Qi Tian, Hongxu Zhang, Peng Liu, Yuzhou Wu, Runze |
| Author_xml | – sequence: 1 givenname: Yuzhou orcidid: 0000-0003-2765-4074 surname: Liu fullname: Liu, Yuzhou email: liuyuzhou@jlu.edu.cn organization: College of Computer Science and Technology, Jilin University, Changchun Jilin, 130012, China – sequence: 2 givenname: Qi surname: Wang fullname: Wang, Qi organization: College of Computer Science and Technology, Jilin University, Changchun Jilin, 130012, China – sequence: 3 givenname: Shuang surname: Jiang fullname: Jiang, Shuang organization: College of Software, Jilin University, Changchun Jilin, 130012, China – sequence: 4 givenname: Runze surname: Wu fullname: Wu, Runze organization: College of Computer Science and Technology, Jilin University, Changchun Jilin, 130012, China – sequence: 5 givenname: Hongxu surname: Tian fullname: Tian, Hongxu organization: College of Software, Jilin University, Changchun Jilin, 130012, China – sequence: 6 givenname: Peng surname: Zhang fullname: Zhang, Peng organization: College of Computer Science and Technology, Jilin University, Changchun Jilin, 130012, China |
| BookMark | eNp9kLtuwzAMRTWkQJO0X9BFP-DU8lMaOhRBX0CKLtkFSqIQGY5USHaK_H3tpHMnEuS9xOVZkYUPHgl5YPmG5ax57DZJOx02RV7U06SsGFuQ5bRps6YqyluySqnL87ypWrYkh8-xH9wxGOip8zbEIwwueGrHdCkh0hTs8AMR6WnsPUZQrnfDmRocUF-0ChIaOjdhOEzyMWqk4A1VzkM8Ux0MpjtyY6FPeP9X12T_-rLfvme7r7eP7fMu04KzjCsGhoMSqAtRtMJwYYUx1qoaVMPRAohCMMuR16htpaAQNaJpObASOZZrUl7P6hhSimjld3THKYVkuZz5yE5e-MiZj7zymVxPVxdOyU4O46xBr9G4OD0pTXD_-n8BO0V3hQ |
| Cites_doi | 10.1016/j.inffus.2024.102748 10.1016/j.cose.2023.103341 10.18653/v1/2022.acl-long.499 10.1145/3289393 10.1016/j.knosys.2025.113341 10.1109/TSE.2024.3421591 10.1145/3654443 10.1016/j.cose.2021.102247 10.1145/3674729 10.1016/j.knosys.2022.110139 10.1109/TSE.2021.3087402 10.1016/j.inffus.2024.102662 10.1016/j.cose.2022.103023 10.1109/TR.2022.3165115 10.1016/j.cose.2024.104059 10.1016/j.ins.2023.03.132 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier B.V. |
| Copyright_xml | – notice: 2025 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.scico.2025.103411 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| ExternalDocumentID | 10_1016_j_scico_2025_103411 S0167642325001509 |
| GroupedDBID | --K --M .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9DU 9JN AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABJNI ABMAC ABWVN ABXDB ACDAQ ACGFS ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADHUB ADMUD ADNMO ADVLN AEBSH AEIPS AEKER AENEX AEUPX AEXQZ AFFNX AFJKZ AFPUW AFTJW AGHFR AGQPQ AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOUOD APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC CS3 DU5 E.L EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HVGLF HZ~ IHE IXB J1W KOM LG9 M26 M41 MO0 N9A O-L O9- OAUVE OK1 OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SDF SDG SDP SES SEW SPC SPCBC SSV SSZ T5K TN5 WUQ XPP ZMT ZY4 ~G- ~HD AAYXX CITATION |
| ID | FETCH-LOGICAL-c981-8b1ad8ab9ec29279d89f9ddffb5ab68efaa9291f8e85ecf4ba295eed78a13e8e3 |
| ISSN | 0167-6423 |
| IngestDate | Thu Nov 27 00:54:52 EST 2025 Wed Dec 10 14:42:30 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Source codes feature Multimodal fusion Software vulnerability detection Binary codes feature |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c981-8b1ad8ab9ec29279d89f9ddffb5ab68efaa9291f8e85ecf4ba295eed78a13e8e3 |
| ORCID | 0000-0003-2765-4074 |
| ParticipantIDs | crossref_primary_10_1016_j_scico_2025_103411 elsevier_sciencedirect_doi_10_1016_j_scico_2025_103411 |
| PublicationCentury | 2000 |
| PublicationDate | May 2026 2026-05-00 |
| PublicationDateYYYYMMDD | 2026-05-01 |
| PublicationDate_xml | – month: 05 year: 2026 text: May 2026 |
| PublicationDecade | 2020 |
| PublicationTitle | Science of computer programming |
| PublicationYear | 2026 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Ni, Guo, Zhu, Xu, Yang (bib0008) 2024 Bhutamapuram (bib0007) 2023 Tao, Su, Ke, Han, Zheng, Wei (bib0035) 2025; 316 Checkmax T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, (2016). arXiv preprint Rahman, Ceka, Mao, Chakraborty, Ray, Le (bib0050) 2024 Luo, Wang, Wang, Tang, Xie, Zhou, Liu, Lu (bib0033) 2023 Wang, Jia, Peng, Huang, Liu (bib0053) 2023; 125 Tsunoda, Monden, Toda, Tahir, Bennin, Nakasai, Nagura, Matsumoto (bib0018) 2022 Nataraj, Karthikeyan, Jacob, Manjunath (bib0023) 2011 Ali, Ur Rehman, Nawaz, Ali, Abbas (bib0043) 2022 Zhang, Wang, Li, Wang, Li, Liu (bib0002) 2024 Jie, Chen, Wang, Voundi Koe, Li, Huang, Wu, Wang (bib0056) 2023; 636 Jagtap, Katragadda, Satelkar (bib0005) 2022 Zhang, Xu, Zhang, Liu, Chen, Yin, Zheng (bib0052) 2024; 33 Yang, Yang, Wong (bib0047) 2024; 50 Mamede, Pinconschi, Abreu (bib0026) 2023 Chakraborty, Krishna, Ding, Ray (bib0027) 2022; 48 W. Zaremba, Recurrent neural network regularization, (2014). arXiv preprint Ni, Yin, Yang, Zhao, Xing, Xia (bib0051) 2023 . Stradowski, Madeyski (bib0006) 2024 Cao, Sun, Wu, Lo, Bo, Li, Liu, Lin, Liu (bib0012) 2024 Wu, Ling, Duan, Luo, Yang (bib0021) 2024 Chen, Hu, Yu, Chen, Xuan, Liu, Filkov (bib0030) 2020 Öztürk (bib0032) 2024; 146 Iannone, Sellitto, Iaccarino, Ferrucci, De Lucia, Palomba (bib0004) 2024; 33 Zhao, Srivastava, Peng, Chen (bib0037) 2019; 15 Tran, Tran, Le (bib0014) 2025; 163 Zhou, Liu, Siow, Du, Liu (bib0028) 2019 Wen, Chen, Gao, Zhang, Zhang, Liao (bib0031) 2023 Yuan, Lu, Fang, Wu, Zou, Li, Li, Jin (bib0011) 2023 Hussain, Ibrahim (bib0019) 2022 Khanna, Agarwal, Rahul (bib0046) 2022 Li, Liu, Liu, Liu (bib0013) 2025; 114 ChatGPT Yu, Rao, Liu, Lin, Hu, Keung, Zhou, Xiang (bib0045) 2024; 165 Wang, Tang, Tan, Wang, Liu, Fang, Xia, Wang (bib0055) 2024 Li, Wang, Nguyen (bib0009) 2021 Liu, Wang, Xu, Sun, Zhang, Li, Guo (bib0010) 2025; 115 FlawFinder Wen, Wang, Gao, Wang, Liu, Gu (bib0020) 2024 D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, J. Yin, Unixcoder: unified cross-modal pre-training for code representation, (2022). arXiv preprint Zhao, Huang, Dai (bib0054) 2023; 260 Pinhero, M L, Vinod, Visaggio, Aneesh, Abhijith, AnanthaKrishnan (bib0022) 2021; 105 Pradhan, Muduli (bib0044) 2023 Dataset Tao, Su, Wan, Wei, Zheng (bib0034) 2023; 132 Weng, Qin, Lin, Liu, Chen (bib0049) 2024 Chen, Sun, Gong, Hao (bib0025) 2024 Song, Minku, Teng, Yao (bib0003) 2023 Fortify A. Dosovitskiy, An image is worth 16x16 words: transformers for image recognition at scale, (2020). arXiv preprint Cho, van Merriënboer, Gulcehre, Bahdanau, Bougares, Schwenk, Bengio (bib0038) 2014 Wu, Liu, Xiao, Li, Sun, Lin (bib0024) 2023 Fang, Liu, Liu (bib0048) 2022; 71 Stradowski, Madeyski (bib0001) 2024 Zhou (10.1016/j.scico.2025.103411_bib0028) 2019 Fang (10.1016/j.scico.2025.103411_bib0048) 2022; 71 Li (10.1016/j.scico.2025.103411_bib0009) 2021 Weng (10.1016/j.scico.2025.103411_bib0049) 2024 Mamede (10.1016/j.scico.2025.103411_bib0026) 2023 Chen (10.1016/j.scico.2025.103411_bib0030) 2020 10.1016/j.scico.2025.103411_bib0016 10.1016/j.scico.2025.103411_bib0015 10.1016/j.scico.2025.103411_bib0017 Zhao (10.1016/j.scico.2025.103411_bib0037) 2019; 15 Cho (10.1016/j.scico.2025.103411_bib0038) 2014 Bhutamapuram (10.1016/j.scico.2025.103411_bib0007) 2023 Yuan (10.1016/j.scico.2025.103411_bib0011) 2023 Pradhan (10.1016/j.scico.2025.103411_bib0044) 2023 Liu (10.1016/j.scico.2025.103411_bib0010) 2025; 115 Wang (10.1016/j.scico.2025.103411_bib0055) 2024 Stradowski (10.1016/j.scico.2025.103411_bib0001) 2024 Stradowski (10.1016/j.scico.2025.103411_bib0006) 2024 Rahman (10.1016/j.scico.2025.103411_bib0050) 2024 Ni (10.1016/j.scico.2025.103411_bib0051) 2023 Chakraborty (10.1016/j.scico.2025.103411_bib0027) 2022; 48 10.1016/j.scico.2025.103411_bib0029 Tsunoda (10.1016/j.scico.2025.103411_bib0018) 2022 Tran (10.1016/j.scico.2025.103411_bib0014) 2025; 163 Zhao (10.1016/j.scico.2025.103411_bib0054) 2023; 260 Pinhero (10.1016/j.scico.2025.103411_bib0022) 2021; 105 Wu (10.1016/j.scico.2025.103411_bib0021) 2024 Chen (10.1016/j.scico.2025.103411_bib0025) 2024 Khanna (10.1016/j.scico.2025.103411_bib0046) 2022 Li (10.1016/j.scico.2025.103411_bib0013) 2025; 114 Luo (10.1016/j.scico.2025.103411_sbref0029) 2023 Öztürk (10.1016/j.scico.2025.103411_bib0032) 2024; 146 Wu (10.1016/j.scico.2025.103411_bib0024) 2023 10.1016/j.scico.2025.103411_bib0039 Wang (10.1016/j.scico.2025.103411_bib0053) 2023; 125 Nataraj (10.1016/j.scico.2025.103411_bib0023) 2011 10.1016/j.scico.2025.103411_bib0036 10.1016/j.scico.2025.103411_bib0041 Wen (10.1016/j.scico.2025.103411_bib0031) 2023 10.1016/j.scico.2025.103411_bib0040 10.1016/j.scico.2025.103411_bib0042 Tao (10.1016/j.scico.2025.103411_bib0035) 2025; 316 Yang (10.1016/j.scico.2025.103411_bib0047) 2024; 50 Song (10.1016/j.scico.2025.103411_bib0003) 2023 Yu (10.1016/j.scico.2025.103411_bib0045) 2024; 165 Ali (10.1016/j.scico.2025.103411_bib0043) 2022 Tao (10.1016/j.scico.2025.103411_bib0034) 2023; 132 Hussain (10.1016/j.scico.2025.103411_bib0019) 2022 Ni (10.1016/j.scico.2025.103411_bib0008) 2024 Zhang (10.1016/j.scico.2025.103411_bib0052) 2024; 33 Jie (10.1016/j.scico.2025.103411_bib0056) 2023; 636 Cao (10.1016/j.scico.2025.103411_bib0012) 2024 Zhang (10.1016/j.scico.2025.103411_bib0002) 2024 Wen (10.1016/j.scico.2025.103411_bib0020) 2024 Jagtap (10.1016/j.scico.2025.103411_bib0005) 2022 Iannone (10.1016/j.scico.2025.103411_bib0004) 2024; 33 |
| References_xml | – year: 2024 ident: bib0025 article-title: Improving smart contract security with contrastive learning-based vulnerability detection publication-title: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering – reference: ChatGPT, ( – start-page: 1911 year: 2024 end-page: 1918 ident: bib0008 article-title: Function-level vulnerability detection through fusing multi-Modal knowledge publication-title: Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering – volume: 50 start-page: 2054 year: 2024 end-page: 2076 ident: bib0047 article-title: Multi-objective software defect prediction via multi-source uncertain information fusion and multi-task multi-view learning publication-title: IEEE Trans. Softw. Eng. – start-page: 115 year: 2024 end-page: 124 ident: bib0049 article-title: MatsVD: boosting statement-level vulnerability detection via dependency-based attention publication-title: Proceedings of the 15th Asia-Pacific Symposium on Internetware – reference: ). – reference: Fortify, ( – start-page: 1 year: 2022 end-page: 7 ident: bib0005 article-title: Software reliability: development of software defect prediction models using advanced techniques publication-title: 2022 Annual Reliability and Maintainability Symposium (RAMS) – start-page: 1098 year: 2024 end-page: 1110 ident: bib0006 article-title: Bridging the gap between academia and industry in machine learning software defect prediction: thirteen considerations publication-title: Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering – start-page: 259 year: 2023 end-page: 263 ident: bib0007 article-title: Some investigations of machine learning models for software defects publication-title: Proceedings of the 45th International Conference on Software Engineering: Companion Proceedings – start-page: 1369 year: 2022 end-page: 1373 ident: bib0046 article-title: Software change prediction using ensemble learning on object oriented metrics publication-title: 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) – start-page: 1611 year: 2023 end-page: 1622 ident: bib0051 article-title: Distinguishing look-alike innocent and vulnerable code by subtle semantic representation learning and explanation publication-title: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering – volume: 33 year: 2024 ident: bib0052 article-title: DSHGT: dual-supervisors heterogeneous graph transformer–a pioneer study of using heterogeneous graph learning for detecting software vulnerabilities publication-title: ACM Trans. Softw. Eng. Methodol. – volume: 636 year: 2023 ident: bib0056 article-title: A novel extended multimodal AI framework towards vulnerability detection in smart contracts publication-title: Inf. Sci. – year: 2011 ident: bib0023 article-title: Malware images: visualization and automatic classification publication-title: Proceedings of the 8th International Symposium on Visualization for Cyber Security – reference: D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, J. Yin, Unixcoder: unified cross-modal pre-training for code representation, (2022). arXiv preprint – volume: 15 start-page: 1 year: 2019 end-page: 27 ident: bib0037 article-title: Long short-term memory network design for analog computing publication-title: ACM J. Emerg. Technol. Comput. Syst. – year: 2024 ident: bib0050 article-title: Towards causal deep learning for vulnerability detection publication-title: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering – start-page: 670 year: 2022 end-page: 681 ident: bib0018 article-title: Using bandit algorithms for selecting feature reduction techniques in software defect prediction publication-title: Proceedings of the 19th International Conference on Mining Software Repositories – reference: FlawFinder, ( – start-page: 1 year: 2022 end-page: 5 ident: bib0043 article-title: An ensemble model for software defect prediction publication-title: 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) – start-page: 1 year: 2023 end-page: 6 ident: bib0044 article-title: Software defect prediction model using adaboost based random forest technique publication-title: 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) – volume: 114 year: 2025 ident: bib0013 article-title: Detecting android malware: a multimodal fusion method with fine-grained feature publication-title: Inf. Fus. – volume: 125 year: 2023 ident: bib0053 article-title: Binvuldet: detecting vulnerability in binary program via decompiled pseudo code and biLSTM-attention publication-title: Comput. Secur. – volume: 115 year: 2025 ident: bib0010 article-title: Vul-LMGNNs: fusing language models and online-distilled graph neural networks for code vulnerability detection publication-title: Inf. Fus. – start-page: 2262 year: 2023 end-page: 2274 ident: bib0011 article-title: Enhancing deep learning-based vulnerability detection by building behavior graph model publication-title: Proceedings of the 45th International Conference on Software Engineering – start-page: 345 year: 2024 end-page: 357 ident: bib0020 article-title: When less is enough: positive and unlabeled learning model for vulnerability detection publication-title: Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering – start-page: 292 year: 2021 end-page: 303 ident: bib0009 article-title: Vulnerability detection with fine-grained interpretations publication-title: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering – reference: T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, (2016). arXiv preprint – year: 2023 ident: bib0033 article-title: Vulhawk: cross-architecture vulnerability detection with entropy-based binary code search – reference: W. Zaremba, Recurrent neural network regularization, (2014). arXiv preprint – volume: 105 year: 2021 ident: bib0022 article-title: Malware detection employed by visualization and deep neural network publication-title: Comput. Secur. – start-page: 1371 year: 2023 end-page: 1383 ident: bib0024 article-title: Learning program semantics for vulnerability detection via vulnerability-specific inter-procedural slicing publication-title: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering – reference: Checkmax, ( – start-page: 413 year: 2022 end-page: 420 ident: bib0019 article-title: Empirical investigation of role of meta-learning approaches for the improvement of software development process via software fault prediction publication-title: Proceedings of the 26th International Conference on Evaluation and Assessment in Software Engineering – reference: A. Dosovitskiy, An image is worth 16x16 words: transformers for image recognition at scale, (2020). arXiv preprint – start-page: 1724 year: 2014 end-page: 1734 ident: bib0038 article-title: Learning phrase representations using RNN encoder–decoder for statistical machine translation publication-title: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) – volume: 71 start-page: 512 year: 2022 end-page: 526 ident: bib0048 article-title: Gated homogeneous fusion networks with jointed feature extraction for defect prediction publication-title: IEEE Trans. Reliab. – volume: 48 start-page: 3280 year: 2022 end-page: 3296 ident: bib0027 article-title: Deep learning based vulnerability detection: are we there yet? publication-title: IEEE Trans. Softw. Eng. – start-page: 605 year: 2023 end-page: 617 ident: bib0003 article-title: A practical human labeling method for online just-in-time software defect prediction publication-title: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering – start-page: 92 year: 2024 end-page: 103 ident: bib0001 article-title: Costs and benefits of machine learning software defect prediction: industrial case study publication-title: In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, FSE 2024 – start-page: 1932 year: 2024 end-page: 1944 ident: bib0002 article-title: Vuladvisor: natural language suggestion generation for software vulnerability repair publication-title: In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE ’24 – year: 2024 ident: bib0055 article-title: Combining structured static code information and dynamic symbolic traces for software vulnerability prediction publication-title: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering – reference: . – reference: Dataset, ( – volume: 163 year: 2025 ident: bib0014 article-title: DetectVul: a statement-level code vulnerability detection for python publication-title: Fut. Gen. Comput. Syst. – volume: 132 year: 2023 ident: bib0034 article-title: Vulnerability detection through cross-modal feature enhancement and fusion publication-title: Comput. Secur. – volume: 316 year: 2025 ident: bib0035 article-title: Transformer-based statement level vulnerability detection by cross-modal fine-grained features capture publication-title: Knowl. Based Syst. – volume: 260 year: 2023 ident: bib0054 article-title: VULDEFF: vulnerability detection method based on function fingerprints and code differences publication-title: Knowl. Based Syst. – start-page: 2275 year: 2023 end-page: 2286 ident: bib0031 article-title: Vulnerability detection with graph simplification and enhanced graph representation learning publication-title: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) – volume: 165 year: 2024 ident: bib0045 article-title: Improving effort-aware defect prediction by directly learning to rank software modules publication-title: Inf. Softw. Technol. – volume: 33 year: 2024 ident: bib0004 article-title: Early and realistic exploitability prediction of just-disclosed software vulnerabilities: how reliable can it be? publication-title: ACM Trans. Softw. Eng. Methodol. – start-page: 606 year: 2024 end-page: 618 ident: bib0012 article-title: Snopy: bridging sample denoising with causal graph learning for effective vulnerability detection publication-title: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering – start-page: 578 year: 2020 end-page: 589 ident: bib0030 article-title: Software visualization and deep transfer learning for effective software defect prediction publication-title: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering – start-page: 323 year: 2024 end-page: 332 ident: bib0021 article-title: VulDL: tree-based and graph-based neural networks for vulnerability detection and localization publication-title: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering – year: 2019 ident: bib0028 article-title: Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks – year: 2023 ident: bib0026 article-title: A transformer-based IDE plugin for vulnerability detection publication-title: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering – volume: 146 year: 2024 ident: bib0032 article-title: A cosine similarity-based labeling technique for vulnerability type detection using source codes publication-title: Comput. Secur. – volume: 115 year: 2025 ident: 10.1016/j.scico.2025.103411_bib0010 article-title: Vul-LMGNNs: fusing language models and online-distilled graph neural networks for code vulnerability detection publication-title: Inf. Fus. doi: 10.1016/j.inffus.2024.102748 – year: 2024 ident: 10.1016/j.scico.2025.103411_bib0050 article-title: Towards causal deep learning for vulnerability detection – start-page: 413 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0019 article-title: Empirical investigation of role of meta-learning approaches for the improvement of software development process via software fault prediction – year: 2023 ident: 10.1016/j.scico.2025.103411_sbref0029 – volume: 163 year: 2025 ident: 10.1016/j.scico.2025.103411_bib0014 article-title: DetectVul: a statement-level code vulnerability detection for python publication-title: Fut. Gen. Comput. Syst. – start-page: 1371 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0024 article-title: Learning program semantics for vulnerability detection via vulnerability-specific inter-procedural slicing – volume: 132 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0034 article-title: Vulnerability detection through cross-modal feature enhancement and fusion publication-title: Comput. Secur. doi: 10.1016/j.cose.2023.103341 – start-page: 2275 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0031 article-title: Vulnerability detection with graph simplification and enhanced graph representation learning – ident: 10.1016/j.scico.2025.103411_bib0040 – ident: 10.1016/j.scico.2025.103411_bib0029 – start-page: 345 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0020 article-title: When less is enough: positive and unlabeled learning model for vulnerability detection – ident: 10.1016/j.scico.2025.103411_bib0016 doi: 10.18653/v1/2022.acl-long.499 – start-page: 92 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0001 article-title: Costs and benefits of machine learning software defect prediction: industrial case study – ident: 10.1016/j.scico.2025.103411_bib0015 – year: 2023 ident: 10.1016/j.scico.2025.103411_bib0026 article-title: A transformer-based IDE plugin for vulnerability detection – volume: 15 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.scico.2025.103411_bib0037 article-title: Long short-term memory network design for analog computing publication-title: ACM J. Emerg. Technol. Comput. Syst. doi: 10.1145/3289393 – start-page: 1724 year: 2014 ident: 10.1016/j.scico.2025.103411_bib0038 article-title: Learning phrase representations using RNN encoder–decoder for statistical machine translation – start-page: 1369 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0046 article-title: Software change prediction using ensemble learning on object oriented metrics – start-page: 115 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0049 article-title: MatsVD: boosting statement-level vulnerability detection via dependency-based attention – start-page: 1611 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0051 article-title: Distinguishing look-alike innocent and vulnerable code by subtle semantic representation learning and explanation – year: 2024 ident: 10.1016/j.scico.2025.103411_bib0055 article-title: Combining structured static code information and dynamic symbolic traces for software vulnerability prediction – volume: 316 year: 2025 ident: 10.1016/j.scico.2025.103411_bib0035 article-title: Transformer-based statement level vulnerability detection by cross-modal fine-grained features capture publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2025.113341 – year: 2011 ident: 10.1016/j.scico.2025.103411_bib0023 article-title: Malware images: visualization and automatic classification – start-page: 1 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0005 article-title: Software reliability: development of software defect prediction models using advanced techniques – ident: 10.1016/j.scico.2025.103411_bib0017 – ident: 10.1016/j.scico.2025.103411_bib0042 – volume: 50 start-page: 2054 issue: 8 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0047 article-title: Multi-objective software defect prediction via multi-source uncertain information fusion and multi-task multi-view learning publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2024.3421591 – year: 2019 ident: 10.1016/j.scico.2025.103411_bib0028 – volume: 33 issue: 6 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0004 article-title: Early and realistic exploitability prediction of just-disclosed software vulnerabilities: how reliable can it be? publication-title: ACM Trans. Softw. Eng. Methodol. doi: 10.1145/3654443 – start-page: 323 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0021 article-title: VulDL: tree-based and graph-based neural networks for vulnerability detection and localization – volume: 105 year: 2021 ident: 10.1016/j.scico.2025.103411_bib0022 article-title: Malware detection employed by visualization and deep neural network publication-title: Comput. Secur. doi: 10.1016/j.cose.2021.102247 – ident: 10.1016/j.scico.2025.103411_bib0036 – start-page: 670 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0018 article-title: Using bandit algorithms for selecting feature reduction techniques in software defect prediction – start-page: 605 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0003 article-title: A practical human labeling method for online just-in-time software defect prediction – volume: 33 issue: 8 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0052 article-title: DSHGT: dual-supervisors heterogeneous graph transformer–a pioneer study of using heterogeneous graph learning for detecting software vulnerabilities publication-title: ACM Trans. Softw. Eng. Methodol. doi: 10.1145/3674729 – ident: 10.1016/j.scico.2025.103411_bib0039 – start-page: 1 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0043 article-title: An ensemble model for software defect prediction – start-page: 292 year: 2021 ident: 10.1016/j.scico.2025.103411_bib0009 article-title: Vulnerability detection with fine-grained interpretations – volume: 165 issue: C year: 2024 ident: 10.1016/j.scico.2025.103411_bib0045 article-title: Improving effort-aware defect prediction by directly learning to rank software modules publication-title: Inf. Softw. Technol. – volume: 260 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0054 article-title: VULDEFF: vulnerability detection method based on function fingerprints and code differences publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.110139 – start-page: 2262 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0011 article-title: Enhancing deep learning-based vulnerability detection by building behavior graph model – volume: 48 start-page: 3280 issue: 9 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0027 article-title: Deep learning based vulnerability detection: are we there yet? publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.2021.3087402 – start-page: 606 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0012 article-title: Snopy: bridging sample denoising with causal graph learning for effective vulnerability detection – volume: 114 year: 2025 ident: 10.1016/j.scico.2025.103411_bib0013 article-title: Detecting android malware: a multimodal fusion method with fine-grained feature publication-title: Inf. Fus. doi: 10.1016/j.inffus.2024.102662 – ident: 10.1016/j.scico.2025.103411_bib0041 – volume: 125 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0053 article-title: Binvuldet: detecting vulnerability in binary program via decompiled pseudo code and biLSTM-attention publication-title: Comput. Secur. doi: 10.1016/j.cose.2022.103023 – start-page: 1932 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0002 article-title: Vuladvisor: natural language suggestion generation for software vulnerability repair – year: 2024 ident: 10.1016/j.scico.2025.103411_bib0025 article-title: Improving smart contract security with contrastive learning-based vulnerability detection – start-page: 1098 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0006 article-title: Bridging the gap between academia and industry in machine learning software defect prediction: thirteen considerations – start-page: 1911 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0008 article-title: Function-level vulnerability detection through fusing multi-Modal knowledge – start-page: 578 year: 2020 ident: 10.1016/j.scico.2025.103411_bib0030 article-title: Software visualization and deep transfer learning for effective software defect prediction – volume: 71 start-page: 512 issue: 2 year: 2022 ident: 10.1016/j.scico.2025.103411_bib0048 article-title: Gated homogeneous fusion networks with jointed feature extraction for defect prediction publication-title: IEEE Trans. Reliab. doi: 10.1109/TR.2022.3165115 – volume: 146 year: 2024 ident: 10.1016/j.scico.2025.103411_bib0032 article-title: A cosine similarity-based labeling technique for vulnerability type detection using source codes publication-title: Comput. Secur. doi: 10.1016/j.cose.2024.104059 – start-page: 259 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0007 article-title: Some investigations of machine learning models for software defects – start-page: 1 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0044 article-title: Software defect prediction model using adaboost based random forest technique – volume: 636 year: 2023 ident: 10.1016/j.scico.2025.103411_bib0056 article-title: A novel extended multimodal AI framework towards vulnerability detection in smart contracts publication-title: Inf. Sci. doi: 10.1016/j.ins.2023.03.132 |
| SSID | ssj0006471 |
| Score | 2.4292567 |
| Snippet | •The study detects software vulnerability based on both source and binary codes.•A novel fusion strategy is designed to accommodate the characteristics of two... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 103411 |
| SubjectTerms | Binary codes feature Multimodal fusion Software vulnerability detection Source codes feature |
| Title | Multimodal information fusion for software vulnerability detection based on both source and binary codes |
| URI | https://dx.doi.org/10.1016/j.scico.2025.103411 |
| Volume | 250 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0167-6423 databaseCode: AIEXJ dateStart: 20211211 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0006471 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swECZcu0OXNn0hSZOAQzdXhfWgRY5BkKLJELSFgXoTSJGCbTiSYVtukrG_vEdSlOTWMJKhiyAQJi3oPh3vjt_dIfSRkDQII8nhS-PCi7gKPZrqI3YemWonEZHSNJuIb27oeMy-dTq_XS7MZh7nOb27Y4v_KmoYA2Hr1NkniLteFAbgHoQOVxA7XB8leJNSe1tIU02jzk3sZ-XKsQpXoHp_acbXppzrotOGH3vfl2qtbONwvbVJfYwgCk1gN_F9c8ogbPauzoNftc1apyEqjrpuE-GYX7dub9Ssn2lpVH75MCnKJpRv1c33ac3mmbog9qTkzeyfZvKPMn9Q7VhFMGyYgS58CWoZPJ6wrX8DW3m20qD-APZVf6dyt3GGGbj98JGAax-Qz82vt0tp_7XF1cRDx2mbJWaRRC-S2EWeoV4QE0a7qHd-dTm-rvfzoXXb62d3tasMS_CfZ9lt37RsltEBelk5G_jcguQ16qj8DXrlGnngSmpv0aTBDG5hBlvMYBjADjN4CzO4xgw2mMH6BjCDLWYwYAZbzGCDmXdo9OVydPHVq1pweCmjvkeFzyXlgqk0YEHMJGUZkzLLBOFiSFXGOZjXfkYVJSrNIsEDRsDqiin3Q0VV-B518yJXhwiDmZgp7nPFIhERFTIxEHEkRAoefwx-wxH65N5bsrCFVpI90jpCQ_duk8pWtDZgAmjZN_H4af_zAb1ogHyCuutlqU7R83Sznq6WZxVU_gCVsJGL |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodal+information+fusion+for+software+vulnerability+detection+based+on+both+source+and+binary+codes&rft.jtitle=Science+of+computer+programming&rft.au=Liu%2C+Yuzhou&rft.au=Wang%2C+Qi&rft.au=Jiang%2C+Shuang&rft.au=Wu%2C+Runze&rft.date=2026-05-01&rft.issn=0167-6423&rft.volume=250&rft.spage=103411&rft_id=info:doi/10.1016%2Fj.scico.2025.103411&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_scico_2025_103411 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-6423&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-6423&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-6423&client=summon |