{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:00:14Z","timestamp":1776074414023,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Inf. Secur."],"DOI":"10.1007\/s10207-026-01213-5","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T11:20:25Z","timestamp":1770636025000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing security vulnerabilities in a docker-enabled federated learning framework with hyperparameter tuning for software bug prediction"],"prefix":"10.1007","volume":"25","author":[{"given":"Ruchika","family":"Malhotra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anjali","family":"Bansal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marouane","family":"Kessentini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"issue":"1","key":"1213_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10515-024-00424-1","volume":"31","author":"Z Li","year":"2024","unstructured":"Li, Z., Niu, J., Jing, X.-Y.: Software defect prediction: future directions and challenges. Autom. Softw. Eng. 31(1), 19 (2024)","journal-title":"Autom. Softw. Eng."},{"key":"1213_CR2","doi-asserted-by":"publisher","first-page":"111537","DOI":"10.1016\/j.jss.2022.111537","volume":"195","author":"G Giray","year":"2023","unstructured":"Giray, G., Bennin, K.E., K\u00f6ksal, \u00d6., Babur, \u00d6., Tekinerdogan, B.: On the use of deep learning in software defect prediction. J. Syst. Softw. 195, 111537 (2023)","journal-title":"J. Syst. Softw."},{"key":"1213_CR3","doi-asserted-by":"publisher","first-page":"106775","DOI":"10.1016\/j.knosys.2021.106775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl. Based Syst. 216, 106775 (2021)","journal-title":"Knowl. Based Syst."},{"key":"1213_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04266-0","author":"R Malhotra","year":"2024","unstructured":"Malhotra, R., Bansal, A., Kessentini, M.: Deployment and performance monitoring of docker based federated learning framework for software defect prediction. Cluster Comput. (2024). https:\/\/doi.org\/10.1007\/s10586-024-04266-0","journal-title":"Cluster Comput."},{"key":"1213_CR5","doi-asserted-by":"crossref","unstructured":"Komalasari, A., Candra, M.Z.C.: Improving defect prediction using combination of software metrics, in 2022 International Conference on Data and Software Engineering (ICoDSE) (2022) pp. 89\u201394","DOI":"10.1109\/ICoDSE56892.2022.9971813"},{"issue":"2","key":"1213_CR6","doi-asserted-by":"publisher","first-page":"169","DOI":"10.35882\/jeeemi.v6i2.388","volume":"6","author":"AM Akbar","year":"2024","unstructured":"Akbar, A.M., Herteno, R., Saputro, S.W., Faisal, M.R., Nugroho, R.A.: Optimizing software defect prediction models: integrating hybrid grey wolf and particle swarm optimization for enhanced feature selection with popular gradient boosting algorithm. J Electron Electromed Eng Med Inf 6(2), 169\u2013181 (2024)","journal-title":"J Electron Electromed Eng Med Inf"},{"issue":"3","key":"1213_CR7","doi-asserted-by":"publisher","first-page":"3615","DOI":"10.1007\/s10586-023-04170-z","volume":"27","author":"NAA Khleel","year":"2024","unstructured":"Khleel, N.A.A., Neh\u00e9z, K.: Software defect prediction using a bidirectional LSTM network combined with oversampling techniques. Cluster Comput. 27(3), 3615\u20133638 (2024)","journal-title":"Cluster Comput."},{"key":"1213_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04599-w","author":"R Malhotra","year":"2024","unstructured":"Malhotra, R., Khan, K.: A novel software defect prediction model using two-phase grey wolf optimisation for feature selection. Cluster Comput. (2024). https:\/\/doi.org\/10.1007\/s10586-024-04599-w","journal-title":"Cluster Comput."},{"key":"1213_CR9","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.compeleceng.2018.02.043","volume":"67","author":"GR Choudhary","year":"2018","unstructured":"Choudhary, G.R., Kumar, S., Kumar, K., Mishra, A., Catal, C.: Empirical analysis of change metrics for software fault prediction. Comput. Electr. Eng. 67, 15\u201324 (2018)","journal-title":"Comput. Electr. Eng."},{"issue":"2","key":"1213_CR10","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1002\/spe.3274","volume":"54","author":"K Zhu","year":"2024","unstructured":"Zhu, K., Zhang, N., Jiang, C., Zhu, D.: IMDAC: a robust intelligent software defect prediction model via multi-objective optimization and end-to-end hybrid deep learning networks. Softw. Pract. Exp. 54(2), 308\u2013333 (2024)","journal-title":"Softw. Pract. Exp."},{"key":"1213_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2024.3356515","author":"J Chen","year":"2024","unstructured":"Chen, J., Xu, J., Cai, S., Wang, X., Chen, H., Li, Z.: Software defect prediction approach based on a diversity ensemble combined with neural network. IEEE Trans. Reliab. (2024). https:\/\/doi.org\/10.1109\/TR.2024.3356515","journal-title":"IEEE Trans. Reliab."},{"issue":"6","key":"1213_CR12","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.3390\/electronics13061105","volume":"13","author":"H Chen","year":"2024","unstructured":"Chen, H., Yang, L., Wang, A.: Efficient cross-project software defect prediction based on federated meta-learning. Electronics 13(6), 1105 (2024)","journal-title":"Electronics"},{"key":"1213_CR13","doi-asserted-by":"publisher","first-page":"29530","DOI":"10.1109\/ACCESS.2021.3058886","volume":"9","author":"A Wang","year":"2022","unstructured":"Wang, A., et al.: Heterogeneous defect prediction based on federated transfer learning via knowledge distillation. IEEE Access 9, 29530\u201329540 (2022)","journal-title":"IEEE Access"},{"key":"1213_CR14","doi-asserted-by":"publisher","first-page":"87832","DOI":"10.1109\/ACCESS.2022.3195039","volume":"10","author":"A Wang","year":"2022","unstructured":"Wang, A., Zhao, Y., Li, G., Zhang, J., Wu, H., Iwahori, Y.: Heterogeneous defect prediction based on federated reinforcement learning via gradient clustering. IEEE Access 10, 87832\u201387843 (2022)","journal-title":"IEEE Access"},{"key":"1213_CR15","doi-asserted-by":"publisher","first-page":"23739","DOI":"10.1109\/ACCESS.2023.3253765","volume":"11","author":"A Wang","year":"2023","unstructured":"Wang, A., Zhao, Y., Yang, L., Wu, H., Iwahori, Y.: Heterogeneous defect prediction algorithm combined with federated sparse compression. IEEE Access 11, 23739\u201323753 (2023)","journal-title":"IEEE Access"},{"key":"1213_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00607-023-01179-5","author":"A Nandi","year":"2023","unstructured":"Nandi, A., Xhafa, F., Kumar, R.: A Docker-based federated learning framework design and deployment for multi-modal data stream classification. Computing (2023). https:\/\/doi.org\/10.1007\/s00607-023-01179-5","journal-title":"Computing"},{"key":"1213_CR17","doi-asserted-by":"crossref","unstructured":"Yamamoto, H., Wang, D., Rajbahadur, G.K., Kondo, M., Kamei, Y., Ubayashi, N.: Towards privacy preserving cross project defect prediction with federated learning in 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (2023) pp. 485\u2013496","DOI":"10.1109\/SANER56733.2023.00052"},{"issue":"5","key":"1213_CR18","doi-asserted-by":"publisher","first-page":"376","DOI":"10.3390\/machines10050376","volume":"10","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Wang, J., Wang, Z.: Bearing faulty prediction method based on federated transfer learning and knowledge distillation. Machines 10(5), 376 (2022)","journal-title":"Machines"},{"issue":"5","key":"1213_CR19","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TSE.2020.3034721","volume":"48","author":"Y He","year":"2020","unstructured":"He, Y., Meng, G., Chen, K., Hu, X., He, J.: Towards security threats of deep learning systems: a survey. IEEE Trans. Softw. Eng. 48(5), 1743\u20131770 (2020)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"1213_CR20","doi-asserted-by":"crossref","unstructured":"Xiao, Q., Li, K., Zhang, D., Xu, W.: Security risks in deep learning implementations, in 2018 IEEE Security and privacy workshops (SPW) (2018) pp. 123\u2013128","DOI":"10.1109\/SPW.2018.00027"},{"key":"1213_CR21","doi-asserted-by":"publisher","first-page":"102948","DOI":"10.1016\/j.cose.2022.102948","volume":"124","author":"K Filus","year":"2023","unstructured":"Filus, K., Doma\u0144ska, J.: Software vulnerabilities in TensorFlow-based deep learning applications. Comput. Secur. 124, 102948 (2023)","journal-title":"Comput. Secur."},{"key":"1213_CR22","doi-asserted-by":"crossref","unstructured":"Harzevili, N.S., Shin, J., Wang, J., Wang, S., Nagappan, N.: Characterizing and understanding software security vulnerabilities in machine learning libraries, in 2023 IEEE\/ACM 20th International Conference on Mining Software Repositories (MSR) (2023) pp. 27\u201338","DOI":"10.1109\/MSR59073.2023.00018"},{"key":"1213_CR23","doi-asserted-by":"publisher","unstructured":"Javed, O., Toor, S.: Understanding the quality of container security vulnerability detection tools (2021) arXiv. https:\/\/doi.org\/10.48550\/ARXIV.2101.03844","DOI":"10.48550\/ARXIV.2101.03844"},{"key":"1213_CR24","doi-asserted-by":"crossref","unstructured":"Malhotra, R., Bansal, A., Kessentini, M.: Vulnerability analysis of docker hub official images and verified images, in 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE) (2023) pp. 150\u2013155","DOI":"10.1109\/SOSE58276.2023.00025"},{"key":"1213_CR25","doi-asserted-by":"publisher","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li, L., Fan, Y., Tse, M., Lin, K.-Y.: A review of applications in federated learning. Comput. Ind. Eng. 149, 106854 (2020)","journal-title":"Comput. Ind. Eng."},{"issue":"5","key":"1213_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450288","volume":"54","author":"SK Lo","year":"2021","unstructured":"Lo, S.K., Lu, Q., Wang, C., Paik, H.-Y., Zhu, L.: A systematic literature review on federated machine learning: from a software engineering perspective. ACM Computing Surveys (CSUR) 54(5), 1\u201339 (2021)","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"1213_CR27","doi-asserted-by":"publisher","unstructured":"A\u00efvodji, U.M., Gambs, S., Martin, A.: IOTFLA\u202f: A secured and privacy-preserving smart home architecture implementing federated learning, in 2019 IEEE Security and Privacy Workshops (SPW) (2019) pp. 175\u2013180. https:\/\/doi.org\/10.1109\/SPW.2019.00041","DOI":"10.1109\/SPW.2019.00041"},{"issue":"6","key":"1213_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/MCOM.001.1900461","volume":"58","author":"S Niknam","year":"2020","unstructured":"Niknam, S., Dhillon, H.S., Reed, J.H.: Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun. Mag. 58(6), 46\u201351 (2020)","journal-title":"IEEE Commun. Mag."},{"key":"1213_CR29","doi-asserted-by":"publisher","DOI":"10.1145\/3381006","author":"J Feng","year":"2020","unstructured":"Feng, J., Rong, C., Sun, F., Guo, D., Li, Y.: PMF: a privacy-preserving human mobility prediction framework via federated learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (2020). https:\/\/doi.org\/10.1145\/3381006","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"1213_CR30","doi-asserted-by":"crossref","unstructured":"Van Rijn, J.N., Hutter, F.: Hyperparameter importance across datasets, in Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining (2018) pp. 2367\u20132376","DOI":"10.1145\/3219819.3220058"},{"issue":"3","key":"1213_CR31","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/S11219-014-9241-7","volume":"23","author":"L Madeyski","year":"2015","unstructured":"Madeyski, L., Jureczko, M.: Which process metrics can significantly improve defect prediction models? An empirical study. Softw. Qual. J. 23(3), 393\u2013422 (2015). https:\/\/doi.org\/10.1007\/S11219-014-9241-7","journal-title":"Softw. Qual. J."},{"key":"1213_CR32","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1007\/s00521-015-1962-4","volume":"27","author":"VK Kamboj","year":"2016","unstructured":"Kamboj, V.K.: A novel hybrid PSO--GWO approach for unit commitment problem. Neural Comput. Appl. 27, 1643\u20131655 (2016)","journal-title":"Neural Comput. Appl."},{"issue":"4","key":"1213_CR33","first-page":"1275","volume":"20","author":"N Nahak","year":"2017","unstructured":"Nahak, N., Mallick, R.K.: Damping of power system oscillations by a novel DE-GWO optimized dual UPFC controller. Eng. Sci. Technol. Int. J. 20(4), 1275\u20131284 (2017)","journal-title":"Eng. Sci. Technol. Int. J."},{"issue":"4","key":"1213_CR34","doi-asserted-by":"publisher","first-page":"4808","DOI":"10.1007\/s11227-023-05643-z","volume":"80","author":"A Dabba","year":"2024","unstructured":"Dabba, A., Tari, A., Meftali, S.: A novel grey wolf optimization algorithm based on geometric transformations for gene selection and cancer classification. J. Supercomput. 80(4), 4808\u20134840 (2024)","journal-title":"J. Supercomput."},{"issue":"2","key":"1213_CR35","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1108\/IJICC-11-2023-0385","volume":"17","author":"M Mustaqeem","year":"2024","unstructured":"Mustaqeem, M., Mustajab, S., Alam, M.: A hybrid approach for optimizing software defect prediction using a grey wolf optimization and multilayer perceptron. Int. J. Intell. Comput. Cybern. 17(2), 436\u2013464 (2024)","journal-title":"Int. J. Intell. Comput. Cybern."},{"issue":"1","key":"1213_CR36","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s12652-022-04433-4","volume":"14","author":"S Kili\u00e7arslan","year":"2023","unstructured":"Kili\u00e7arslan, S.: PSO+ GWO: a hybrid particle swarm optimization and grey wolf optimization based algorithm for fine-tuning hyper-parameters of convolutional neural networks for cardiovascular disease detection. J. Ambient. Intell. Humaniz. Comput. 14(1), 87\u201397 (2023)","journal-title":"J. Ambient. Intell. Humaniz. Comput."}],"container-title":["International Journal of Information Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-026-01213-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10207-026-01213-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10207-026-01213-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:20:12Z","timestamp":1776072012000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10207-026-01213-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1213"],"URL":"https:\/\/doi.org\/10.1007\/s10207-026-01213-5","relation":{},"ISSN":["1615-5270"],"issn-type":[{"value":"1615-5270","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]},"assertion":[{"value":"31 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"49"}}