{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:04:53Z","timestamp":1784300693240,"version":"3.55.0"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cyber Security Cooperative Research Centre"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The use of artificial intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which has opened it up to a myriad of privacy, trust, and legal issues. Moreover, organizations have been loath to share emails, given the risk of leaking commercially sensitive information. Consequently, it has been difficult to obtain sufficient emails to train a global AI model efficiently. Accordingly, privacy-preserving distributed and collaborative machine learning, particularly federated learning (FL), is a desideratum. As it is already prevalent in the healthcare sector, questions remain regarding the effectiveness and efficacy of FL-based phishing detection within the context of multi-organization collaborations. To the best of our knowledge, the work herein was the first to investigate the use of FL in phishing email detection. This study focused on building upon a deep neural network model, particularly recurrent convolutional neural network (RNN) and bidirectional encoder representations from transformers (BERT), for phishing email detection. We analyzed the FL-entangled learning performance in various settings, including (i) a balanced and asymmetrical data distribution among organizations and (ii) scalability. Our results corroborated the comparable performance statistics of FL in phishing email detection to centralized learning for balanced datasets and low organizational counts. Moreover, we observed a variation in performance when increasing the organizational counts. For a fixed total email dataset, the global RNN-based model had a 1.8% accuracy decrease when the organizational counts were increased from 2 to 10. In contrast, BERT accuracy increased by 0.6% when increasing organizational counts from 2 to 5. However, if we increased the overall email dataset by introducing new organizations in the FL framework, the organizational level performance improved by achieving a faster convergence speed. In addition, FL suffered in its overall global model performance due to highly unstable outputs if the email dataset distribution was highly asymmetric.<\/jats:p>","DOI":"10.3390\/s23094346","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T02:02:23Z","timestamp":1682647343000},"page":"4346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Evaluation of Federated Learning in Phishing Email Detection"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-3378","authenticated-orcid":false,"given":"Chandra","family":"Thapa","sequence":"first","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1561-0288","authenticated-orcid":false,"given":"Jun Wen","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering, The University of New South Wales, Sydney 2052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9695-7947","authenticated-orcid":false,"given":"Alsharif","family":"Abuadbba","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"},{"name":"Cyber Security Cooperative Research Centre, Australian Capital Territory 2604, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yansong","family":"Gao","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6353-8359","authenticated-orcid":false,"given":"Seyit","family":"Camtepe","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surya","family":"Nepal","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"},{"name":"Cyber Security Cooperative Research Centre, Australian Capital Territory 2604, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-6282","authenticated-orcid":false,"given":"Mahathir","family":"Almashor","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, Data61, Sydney 2122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","unstructured":"Retruster Ltd (2021, January 24). 2019 Phishing Statistics and Email Fraud Statistics. Available online: https:\/\/retruster.com\/blog\/2019-phishing-and-email-fraud-statistics.html."},{"key":"ref_2","unstructured":"Mathews, L. (2021, February 07). Phishing Scams Cost American Businesses Half A Billion Dollars A Year. Available online: https:\/\/www.forbes.com\/sites\/leemathews\/2017\/05\/05\/phishing-scams-cost-american-businesses-half-a-billion-dollars-a-year\/#133f645b3fa1."},{"key":"ref_3","unstructured":"Muncaster, P. (2021, February 08). COVID19 Drives Phishing Emails Up 667% in Under a Month. Available online: https:\/\/www.infosecurity-magazine.com\/news\/covid19-drive-phishing-emails-667?utm_source=twitterfeed&utm_medium=twitter."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e01802","DOI":"10.1016\/j.heliyon.2019.e01802","article-title":"Machine learning for email spam filtering: Review, approaches and open research problems","volume":"5","author":"Dada","year":"2019","journal-title":"Heliyon"},{"key":"ref_5","unstructured":"Hiransha, M., Unnithan, N.A., Vinayakumar, R., Soman, K., and Verma, A.D.R. (2018, January 21). Deep Learning Based Phishing E-mail Detection CEN-Deepspam. Proceedings of the 1st AntiPhishing Shared Pilot at 4th ACM IWSPA, Tempe, AZ, USA."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56329","DOI":"10.1109\/ACCESS.2019.2913705","article-title":"Phishing email detection using improved RCNN model with multilevel vectors and attention mechanism","volume":"7","author":"Fang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/COMST.2019.2957750","article-title":"SoK: A Comprehensive Reexamination of Phishing Research From the Security Perspective","volume":"22","author":"Das","year":"2020","journal-title":"Commun. Surv. Tuts."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1007\/s11280-017-0524-3","article-title":"Resisting re-identification mining on social graph data","volume":"21","author":"Gao","year":"2018","journal-title":"World Wide Web"},{"key":"ref_10","unstructured":"Ho, G., Cidon, A., Gavish, L., Schweighauser, M., Paxson, V., Savage, S., Voelker, G.M., and Wagner, D. (2019, January 14\u201316). Detecting and characterizing lateral phishing at scale. Proceedings of the 28th USENIX Security Symposium (USENIX Security 19), Santa Clara, CA, USA."},{"key":"ref_11","unstructured":"Shastri, S., Wasserman, M., and Chidambaram, V. (2019). Proceedings of the 11th USENIX Conference on Hot Topics in Cloud Computing, Renton, WA, USA, 8 July 2019, USENIX Association."},{"key":"ref_12","unstructured":"EU GDPR (2019, November 10). General Data Protection Regulation (GDPR). Available online: https:\/\/www.eugdpr.org\/."},{"key":"ref_13","unstructured":"HIPAA Compliance Assistance (2003). Summary of the HIPAA Privacy Rule."},{"key":"ref_14","first-page":"90:1","article-title":"A survey of Algorithms and Analysis for Adaptive Online Learning","volume":"18","author":"McMahan","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref_15","unstructured":"Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecn\u00fd, J., Mazzocchi, S., and McMahan, H.B. (2019). Towards Federated Learning at Scale: System Design. arXiv."},{"key":"ref_16","unstructured":"Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., and Cummings, R. (2019). Advances and open problems in federated learning. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, Y., Ye, K., Li, L., and Xu, C.Z. (2019, January 25\u201330). FFD: A Federated Learning Based Method for Credit Card Fraud Detection. Proceedings of the International Conference on Big Data, San Diego, CA, USA.","DOI":"10.1007\/978-3-030-23551-2_2"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","article-title":"The Future of Digital Health with Federated Learning","volume":"3","author":"Rieke","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, B.Y., He, C., Zeng, Z., Wang, H., Huang, Y., Soltanolkotabi, M., Ren, X., and Avestimehr, S. (2021). FedNLP: A Research Platform for Federated Learning in Natural Language Processing. arXiv.","DOI":"10.18653\/v1\/2022.findings-naacl.13"},{"key":"ref_20","unstructured":"ReDAS Lab@UH (2020, January 16). First Security and Privacy Analytics Anti-Phishing Shared Task (IWSPA-AP 2018). Available online: https:\/\/dasavisha.github.io\/IWSPA-sharedtask\/."},{"key":"ref_21","unstructured":"Nazario, J. (2020, January 16). Nazario\u2019s Phishing Corpora. Available online: https:\/\/monkey.org\/~jose\/phishing\/."},{"key":"ref_22","unstructured":"CALO Project (2020, January 16). Enron Email Dataset. Available online: http:\/\/www.cs.cmu.edu\/~enron\/."},{"key":"ref_23","unstructured":"Cornell University (2021, January 03). Phis Bowl. Available online: https:\/\/it.cornell.edu\/phish-bowl."},{"key":"ref_24","unstructured":"(2020, January 16). The Dada Engine. Available online: http:\/\/dev.null.org\/dadaengine\/."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., and Zhao, J. (2015, January 25\u201330). Recurrent convolutional neural networks for text classification. Proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"ref_26","unstructured":"HuggingFace (2020, April 09). Bert-Base-Uncased. Available online: https:\/\/huggingface.co\/bert-base-uncased."},{"key":"ref_27","unstructured":"Lee, Y., Saxe, J., and Harang, R. (2020). CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails. arXiv."},{"key":"ref_28","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2020). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv."},{"key":"ref_29","unstructured":"Python Software Foundation (2020, January 18). email.header: Internationalized Headers. Available online: https:\/\/docs.python.org\/3\/library\/email.header.html."},{"key":"ref_30","unstructured":"Secret Labs (2020, January 18). re\u2014Regular Expression Operations. Available online: https:\/\/docs.python.org\/3\/library\/re.html."},{"key":"ref_31","unstructured":"BeautifulSoup Group (2020, January 19). Beautiful Soup. Available online: https:\/\/www.crummy.com\/software\/BeautifulSoup\/."},{"key":"ref_32","unstructured":"(2020, January 19). Online. html.parser\u2014Simple HTML and XHTML Parser. Available online: https:\/\/docs.python.org\/3\/library\/html.parser.html."},{"key":"ref_33","unstructured":"Bird, S., Klein, E., and Loper, E. (2020, January 19). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. Available online: https:\/\/www.nltk.org\/book\/ch02.html."},{"key":"ref_34","unstructured":"(2020, January 20). Online. tf.keras.preprocessing.text.Tokenizer. Available online: https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/keras\/preprocessing\/text\/Tokenizer."},{"key":"ref_35","unstructured":"(2021, January 21). Online. BERT. Available online: https:\/\/huggingface.co\/transformers\/model_doc\/bert.html."},{"key":"ref_36","unstructured":"Keeton, K., and Roscoe, T. (2016). Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA, 2\u20134 November 2016, USENIX Association."},{"key":"ref_37","unstructured":"Keras Team (2020, January 18). Keras: The Python Deep Learning Library. Available online: https:\/\/keras.io\/."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.neucom.2019.01.037","article-title":"Convergence analysis of distributed stochastic gradient descent with shuffling","volume":"337","author":"Meng","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Abu-Nimeh, S., Nappa, D., Wang, X., and Nair, S. (2007, January 4\u20135). A comparison of machine learning techniques for phishing detection. Proceedings of the Anti-Phishing Working Groups 2nd Annual eCrime Researchers Summit, Pittsburgh, PA, USA.","DOI":"10.1145\/1299015.1299021"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3233\/JCS-2010-0371","article-title":"New filtering approaches for phishing email","volume":"18","author":"Bergholz","year":"2010","journal-title":"J. Comput. Secur."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Verma, R.M., Shashidhar, N., and Hossain, N. (2012, January 10\u201312). Detecting Phishing Emails the Natural Language Way. Proceedings of the ESORICS 2012, Pisa, Italy.","DOI":"10.1007\/978-3-642-33167-1_47"},{"key":"ref_42","unstructured":"Vazhayil, A., Harikrishnan, N., Vinayakumar, R., Soman, K., and Verma, A. (2018, January 21). PED-ML: Phishing email detection using classical machine learning techniques. Proceedings of the 1st AntiPhishing Shared Pilot at 4th ACM IWSPA, Tempe, AZ, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TDSC.2018.2864993","article-title":"Learning from the Ones that Got Away: Detecting New Forms of Phishing Attacks","volume":"15","author":"Gutierrez","year":"2018","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_44","unstructured":"Unnithan, N.A., Harikrishnan, N.B., Vinayakumar, R., Soman, K.P., and Sundarakrishna, S. (2018, January 21). Detecting Phishing E-mail using Machine learning techniques CEN-SecureNLP. Proceedings of the 1st AntiPhishing Shared Pilot at 4th ACM IWSPA, Tempe, AZ, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.dss.2018.01.001","article-title":"Detection of online phishing email using dynamic evolving neural network based on reinforcement learning","volume":"107","author":"Smadi","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Li, X. (2017, January 12\u201315). Phishing detection method based on borderline-smote deep belief network. Proceedings of the International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, Guangzhou, China.","DOI":"10.1007\/978-3-319-72395-2_5"},{"key":"ref_47","unstructured":"Nguyen, M., Nguyen, T., and Nguyen, T.H. (2018, January 21). A deep learning model with hierarchical lstms and supervised attention for anti-phishing. Proceedings of the 1st AntiPhishing Shared Pilot at 4th ACM IWSPA, Tempe, AZ, USA."},{"key":"ref_48","unstructured":"Bailey, M., Holz, T., Stamatogiannakis, M., and Ioannidis, S. (2018). Proceedings of the RAID 2018, Crete, Greece, 10\u201312 September 2018, Springer."},{"key":"ref_49","unstructured":"Cidon, A., Gavish, L., Bleier, I., Korshun, N., Schweighauser, M., and Tsitkin, A. (2019, January 14\u201316). High precision detection of business email compromise. Proceedings of the 28th USENIX Security Symposium, Santa Clara, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mohassel, P., and Zhang, Y. (2017, January 22\u201326). SecureML: A System for Scalable Privacy-Preserving Machine Learning. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2017.12"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/popets-2019-0035","article-title":"SecureNN: 3-Party Secure Computation for Neural Network Training","volume":"2019","author":"Wagh","year":"2019","journal-title":"PoPETs"},{"key":"ref_52","unstructured":"Mohassel, P., and Rindal, P. (2018, January 15\u201319). ABY3: A Mixed Protocol Framework for Machine Learning. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Toronto, ON, Canada."},{"key":"ref_53","unstructured":"Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D. (2018). Federated learning for mobile keyboard prediction. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., and Dureau, J. (2019, January 12\u201317). Federated learning for keyword spotting. Proceedings of the ICASSP 2019\u20142019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683546"},{"key":"ref_55","unstructured":"Gao, D., Ju, C., Wei, X., Liu, Y., Chen, T., and Yang, Q. (2019). HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography. arXiv."},{"key":"ref_56","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the AISTATS 2017, Fort Lauderdale, FL, USA."},{"key":"ref_57","unstructured":"Shoham, N., Avidor, T., Keren, A., Israel, N., Benditkis, D., Mor-Yosef, L., and Zeitak, I. (2019). Overcoming Forgetting in Federated Learning on Non-IID Data. arXiv."},{"key":"ref_58","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_59","unstructured":"Yu, F.X., Rawat, A.S., Menon, A.K., and Kumar, S. (2020). Federated Learning with Only Positive Labels. arXiv."},{"key":"ref_60","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020, January 26\u201330). Federated learning with matched averaging. Proceedings of the ICLR, Addis Ababa, Ethiopia."},{"key":"ref_61","unstructured":"Jiang, Y., Kone\u010dn\u00fd, J., Rush, K., and Kannan, S. (2019). Improving Federated Learning Personalization via Model Agnostic Meta Learning. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., and Houmansadr, A. (2019, January 19\u201323). Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00065"},{"key":"ref_63","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V. (2018). How to backdoor federated learning. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., and Zhao, B.Y. (2019, January 19\u201323). Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00031"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Gao, Y., Xu, C., Wang, D., Chen, S., Ranasinghe, D.C., and Nepal, S. (2019, January 9\u201313). Strip: A defence against trojan attacks on deep neural networks. Proceedings of the 35th Annual Computer Security Applications Conference, San Juan, PR, USA.","DOI":"10.1145\/3359789.3359790"},{"key":"ref_66","first-page":"2029","article-title":"Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection","volume":"18","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, C., Hu, C., Wu, T., Zhu, L., and Liu, X. (2022). Achieving Efficient and Privacy-Preserving Neural Network Training and Prediction in Cloud Environments. IEEE Trans. Dependable Secur. Comput., 1\u201312.","DOI":"10.1109\/TDSC.2022.3208706"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Gentry, C. (2009). A Fully Homomorphic Encryption Scheme. [Ph.D. Thesis, Stanford University].","DOI":"10.1145\/1536414.1536440"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1561\/0400000042","article-title":"The Algorithmic Foundations of Differential Privacy","volume":"9","author":"Dwork","year":"2014","journal-title":"Found. Trends Theor. Comput. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1109\/JSAC.2022.3213341","article-title":"FRUIT: A Blockchain-Based Efficient and Privacy-Preserving Quality-Aware Incentive Scheme","volume":"40","author":"Zhang","year":"2022","journal-title":"IEEE J. Sel. Areas Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:25:02Z","timestamp":1760124302000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,27]]},"references-count":70,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094346"],"URL":"https:\/\/doi.org\/10.3390\/s23094346","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,27]]}}}