{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T22:42:42Z","timestamp":1785451362638,"version":"3.56.0"},"reference-count":59,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Prince Sultan University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.<\/jats:p>","DOI":"10.3390\/s23094467","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T02:56:51Z","timestamp":1683255411000},"page":"4467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4639-516X","authenticated-orcid":false,"given":"Iman","family":"Almomani","sequence":"first","affiliation":[{"name":"Computer Science Department, King Abdullah II School for Information Technology, The University of Jordan, Amman 11942, Jordan"},{"name":"Security Engineering Laboratory, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7685-2689","authenticated-orcid":false,"given":"Aala","family":"Alkhayer","sequence":"additional","affiliation":[{"name":"Security Engineering Laboratory, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-2120","authenticated-orcid":false,"given":"Walid","family":"El-Shafai","sequence":"additional","affiliation":[{"name":"Security Engineering Laboratory, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia"},{"name":"Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Almomani, I., AlKhayer, A., and Ahmed, M. (2021, January 6\u20137). An Efficient Machine Learning-based Approach for Android v. 11 Ransomware Detection. Proceedings of the 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CAIDA51941.2021.9425059"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57674","DOI":"10.1109\/ACCESS.2021.3071450","article-title":"Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data","volume":"9","author":"Almomani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"SonicWal (2022, August 04). Sonicwall Cyber Threat Report. Available online: https:\/\/www.sonicwall.com\/2021-cyber-threat-report\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Al-Asli, M., and Ghaleb, T.A. (2019, January 3\u20134). Review of signature-based techniques in antivirus products. Proceedings of the 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia.","DOI":"10.1109\/ICCISci.2019.8716381"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Urooj, U., Maarof, M.A.B., and Al-rimy, B.A.S. (2021, January 29\u201331). A proposed Adaptive Pre-Encryption Crypto-Ransomware Early Detection Model. Proceedings of the 2021 3rd International Cyber Resilience Conference (CRC), Langkawi Island, Malaysia.","DOI":"10.1109\/CRC50527.2021.9392548"},{"key":"ref_6","first-page":"100552","article-title":"Internet of Drones Security: Taxonomies, Open Issues, and Future Directions","volume":"39","author":"Derhab","year":"2022","journal-title":"Veh. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alkhelaiwi, M., Boulila, W., Ahmad, J., Koubaa, A., and Driss, M. (2021). An efficient approach based on privacy-preserving deep learning for satellite image classification. Remote Sens., 13.","DOI":"10.3390\/rs13112221"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105528","DOI":"10.1016\/j.compag.2020.105528","article-title":"Automated sheep facial expression classification using deep transfer learning","volume":"175","author":"Noor","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Al Khayer, A., Almomani, I., and Elkawlak, K. (2020, January 3\u20135). ASAF: Android Static Analysis Framework. Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMART-TECH49988.2020.00053"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abdullah, Z., Muhadi, F.W., Saudi, M.M., Hamid, I.R.A., and Foozy, C.F.M. (2020, January 22\u201323). Android Ransomware Detection Based on Dynamic Obtained Features. Proceedings of the International Conference on Soft Computing and Data Mining, Langkawi, Malaysia.","DOI":"10.1007\/978-3-030-36056-6_12"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118073","DOI":"10.1016\/j.eswa.2022.118073","article-title":"Identification of malware families using stacking of textural features and machine learning","volume":"208","author":"Kumar","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bovenzi, G., Cerasuolo, F., Montieri, A., Nascita, A., Persico, V., and Pescap\u00e9, A. (July, January 30). A comparison of machine and deep learning models for detection and classification of android malware traffic. Proceedings of the 2022 IEEE Symposium on Computers and Communications (ISCC), Rhodes, Greece.","DOI":"10.1109\/ISCC55528.2022.9912986"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3103\/S0146411621020085","article-title":"Design of anomaly-based intrusion detection system using fog computing for IoT network","volume":"55","author":"Kumar","year":"2021","journal-title":"Autom. Control Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.1007\/s13369-020-05181-3","article-title":"Toward design of an intelligent cyber attack detection system using hybrid feature reduced approach for iot networks","volume":"46","author":"Kumar","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9555","DOI":"10.1007\/s12652-020-02696-3","article-title":"A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks","volume":"12","author":"Kumar","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Awan, M.J., Masood, O.A., Mohammed, M.A., Yasin, A., Zain, A.M., Dama\u0161evi\u010dius, R., and Abdulkareem, K.H. (2021). Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention. Electronics, 10.","DOI":"10.3390\/electronics10192444"},{"key":"ref_17","unstructured":"Ben Abdel Ouahab, I., Elaachak, L., and Bouhorma, M. (2022). Big Data Intelligence for Smart Applications, Springer."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e6272","DOI":"10.1002\/cpe.6272","article-title":"A survey on analysis and detection of Android ransomware","volume":"33","author":"Sharma","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Bello-Salau, H., Umoh, I.J., Onumanyi, A.J., Adegboye, M.A., and Salawudeen, A.T. (2022). Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. Appl. Sci., 12.","DOI":"10.3390\/app12031186"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alsoghyer, S., and Almomani, I. (2020, January 4\u20135). On the effectiveness of application permissions for Android ransomware detection. Proceedings of the 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CDMA47397.2020.00022"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.future.2019.09.025","article-title":"Ransomware classification using patch-based CNN and self-attention network on embedded N-grams of opcodes","volume":"110","author":"Zhang","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15647","DOI":"10.1038\/s41598-022-19443-7","article-title":"Ransomware detection using deep learning based unsupervised feature extraction and a cost sensitive Pareto Ensemble classifier","volume":"12","author":"Zahoora","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rahali, A., Lashkari, A.H., Kaur, G., Taheri, L., Gagnon, F., and Massicotte, F. (2020, January 27\u201329). DIDroid: Android Malware Classification and Characterization Using Deep Image Learning. Proceedings of the 2020 the 10th International Conference on Communication and Network Security, Tokyo, Japan.","DOI":"10.1145\/3442520.3442522"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, C.C., Jeng, T.H., and Chen, Y.M. (2020, January 5\u20137). Dynamic Android Malware Analysis with De-Identification of Personal Identifiable Information. Proceedings of the 2020 the 3rd International Conference on Computing and Big Data, Taichung, Taiwan.","DOI":"10.1145\/3418688.3418694"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chew, C.J.W., Kumar, V., Patros, P., and Malik, R. (2020, January 25\u201327). ESCAPADE: Encryption-Type-Ransomware: System Call Based Pattern Detection. Proceedings of the International Conference on Network and System Security, Melbourne, Australia.","DOI":"10.1007\/978-3-030-65745-1_23"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s11416-021-00388-w","article-title":"A framework for supporting ransomware detection and prevention based on hybrid analysis","volume":"17","author":"Mercaldo","year":"2021","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"El-Shafai, W., Almomani, I., and AlKhayer, A. (2021). Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models. Appl. Sci., 11.","DOI":"10.3390\/app11146446"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s40031-020-00499-w","article-title":"Texture-Based Automated Classification of Ransomware","volume":"102","author":"Sharma","year":"2021","journal-title":"J. Inst. Eng. (India) Ser. B"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101748","DOI":"10.1016\/j.cose.2020.101748","article-title":"Image-Based malware classification using ensemble of CNN architectures (IMCEC)","volume":"92","author":"Vasan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102247","DOI":"10.1016\/j.cose.2021.102247","article-title":"Malware Detection employed by Visualization and Deep Neural Network","volume":"105","author":"Pinhero","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"101740","DOI":"10.1016\/j.cose.2020.101740","article-title":"Byte-level malware classification based on markov images and deep learning","volume":"92","author":"Yuan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108601","DOI":"10.1016\/j.compeleceng.2023.108601","article-title":"SINN-RD: Spline interpolation-envisioned neural network-based ransomware detection scheme","volume":"106","author":"Singh","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102659","DOI":"10.1016\/j.cose.2022.102659","article-title":"FeSA: Feature selection architecture for ransomware detection under concept drift","volume":"116","author":"Fernando","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alissa, K.A., Elkamchouchi, D.H., Tarmissi, K., Yafoz, A., Alsini, R., Alghushairy, O., Mohamed, A., and Al Duhayyim, M. (2022). Dwarf mongoose optimization with machine-learning-driven ransomware detection in internet of things environment. Appl. Sci., 12.","DOI":"10.3390\/app12199513"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"12077","DOI":"10.1007\/s00521-022-07096-6","article-title":"Evading behavioral classifiers: A comprehensive analysis on evading ransomware detection techniques","volume":"34","author":"Hitaj","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Herrera-Silva, J.A., and Hern\u00e1ndez-\u00c1lvarez, M. (2023). Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms. Sensors, 23.","DOI":"10.3390\/s23031053"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, H., Park, J., Kwon, H., Jang, K., and Seo, H. (2021). Convolutional Neural Network-Based Cryptography Ransomware Detection for Low-End Embedded Processors. Mathematics, 9.","DOI":"10.3390\/math9070705"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bello-Salau, H., Onumanyi, A., Salawudeen, A., Mu\u2019azu, M., and Oyinbo, A. (2019, January 14\u201317). An examination of different vision based approaches for road anomaly detection. Proceedings of the 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), Zaria, Nigeria.","DOI":"10.1109\/NigeriaComputConf45974.2019.8949646"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2700","DOI":"10.1109\/ACCESS.2022.3140341","article-title":"An Automated Vision-Based Deep Learning Model for Efficient Detection of Android Malware Attacks","volume":"10","author":"Almomani","year":"2022","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, J., and Lee, K. (2022). A method for neutralizing entropy measurement-based ransomware detection technologies using encoding algorithms. Entropy, 24.","DOI":"10.3390\/e24020239"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11416-021-00414-x","article-title":"A novel approach for ransomware detection based on PE header using graph embedding","volume":"18","author":"Manavi","year":"2022","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mahfouz, A.M., Venugopal, D., and Shiva, S.G. (2019, January 25\u201326). Comparative analysis of ML classifiers for network intrusion detection. Proceedings of the Fourth International Congress on Information and Communication Technology, London, UK.","DOI":"10.1007\/978-981-32-9343-4_16"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s12652-020-02167-9","article-title":"Attack classification using feature selection techniques: A comparative study","volume":"12","author":"Thakkar","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_44","first-page":"3529","article-title":"An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications","volume":"72","author":"Alshammri","year":"2022","journal-title":"CMC-Comput. Mater. Contin."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.cose.2018.04.005","article-title":"Malware identification using visualization images and deep learning","volume":"77","author":"Ni","year":"2018","journal-title":"Comput. Secur."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.future.2019.04.044","article-title":"Similarity hash based scoring of portable executable files for efficient malware detection in IoT","volume":"110","author":"Namanya","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_47","unstructured":"Brownlee, J. (2020, January 01). Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow Using Keras; 2016. Available online: https:\/\/www.udemy.com\/course\/deep-learning-with-python-and-keras\/."},{"key":"ref_48","unstructured":"Hodnett, M., and Wiley, J.F. (2020, January 01). R Deep Learning Essentials: A Step-by-Step Guide to Building Deep Learning Models Using TensorFlow, Keras, and MXNet; 2018. Available online: https:\/\/www.amazon.com\/Deep-Learning-Essentials-step-step\/dp\/178899289X."},{"key":"ref_49","unstructured":"Vasilev, I., Slater, D., Spacagna, G., Roelants, P., and Zocca, V. (2020, August 04). Python Deep Learning: Exploring Deep Learning Techniques and Neural Network Architectures with Pytorch, Keras, and TensorFlow; 2019. Available online: https:\/\/searchworks.stanford.edu\/view\/13246756."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"25372","DOI":"10.1109\/JSEN.2021.3067608","article-title":"RDNet: Regression Dense and Attention for Object Detection in Traffic Symbols","volume":"21","author":"Hong","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 million image database for scene recognition","volume":"40","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rezende, E., Ruppert, G., Carvalho, T., Ramos, F., and De Geus, P. (2017, January 18\u201321). Malicious software classification using transfer learning of resnet-50 deep neural network. Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, Mexico.","DOI":"10.1109\/ICMLA.2017.00-19"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Almohaini, R., Almomani, I., and AlKhayer, A. (2021). Hybrid-based analysis impact on ransomware detection for Android systems. Appl. Sci., 11.","DOI":"10.3390\/app112210976"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Alsoghyer, S., and Almomani, I. (2019). Ransomware detection system for Android applications. Electronics, 8.","DOI":"10.3390\/electronics8080868"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:28:57Z","timestamp":1760124537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":59,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094467"],"URL":"https:\/\/doi.org\/10.3390\/s23094467","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]}}}