{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T17:44:08Z","timestamp":1786815848558,"version":"build-2736575974"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T00:00:00Z","timestamp":1642464000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller\u2019s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native\u2013Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.<\/jats:p>","DOI":"10.3390\/s22030709","type":"journal-article","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T22:47:32Z","timestamp":1642546052000},"page":"709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6766-1749","authenticated-orcid":false,"given":"Senthil","family":"Prabakaran","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore 641032, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramalakshmi","family":"Ramar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9103-1803","authenticated-orcid":false,"given":"Irshad","family":"Hussain","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6939-4683","authenticated-orcid":false,"given":"Balasubramanian Prabhu","family":"Kavin","sequence":"additional","affiliation":[{"name":"Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai 600116, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8194-9354","authenticated-orcid":false,"given":"Sultan S.","family":"Alshamrani","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9165-7600","authenticated-orcid":false,"given":"Ahmed Saeed","family":"AlGhamdi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0008-9394","authenticated-orcid":false,"given":"Abdullah","family":"Alshehri","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Al Baha University, P.O. 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Electronics, 10.","DOI":"10.3390\/electronics10030323"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1007\/s10586-018-1840-9","article-title":"Performance evaluation of revised virtual resources allocation scheme in network function virtualization (NFV) networks","volume":"22","author":"Kim","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/MNET.2018.1700467","article-title":"Enabling efficient service function chaining by integrating NFV and SDN: Architecture, challenges and opportunities","volume":"32","author":"Zhang","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.yofte.2016.11.012","article-title":"Performance verification of network function virtualization in software defined optical transport networks","volume":"33","author":"Zhao","year":"2016","journal-title":"Opt. Fiber Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/TNSM.2017.2761860","article-title":"An SDN\/NFV platform for personal cloud services","volume":"14","author":"Bruschi","year":"2017","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abbasi, A.A., Al-qaness, M.A.A., Elaziz, M.A., Khalil, H.A., and Kim, S. (2019). Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services. Electronics, 8.","DOI":"10.3390\/electronics8090928"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ahmad, F., Ahmad, A., Hussain, I., Uthansakul, P., and Khan, S. (2020). Cooperation Based Proactive Caching in Multi-Tier Cellular Networks. Appl. Sci., 10.","DOI":"10.3390\/app10186145"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zaman, S., Hussain, I., and Singh, D. (2019). Fast Computation of Integrals with Fourier-Type Oscillator Involving Stationary Point. Mathematics, 7.","DOI":"10.3390\/math7121160"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101689","DOI":"10.1016\/j.is.2020.101689","article-title":"On the composition of the long tail of business processes: Implications from a process mining study","volume":"97","author":"Fischer","year":"2020","journal-title":"Inf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.comcom.2021.01.018","article-title":"SDN\/NFV architectures for edge-cloud oriented IoT: A systematic review","volume":"169","author":"Ray","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s11036-017-0905-y","article-title":"SDN and NFV as Enabler for the Distributed Network Cloud","volume":"23","author":"Hoffmann","year":"2017","journal-title":"Mob. Netw. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hussain, I., Ullah, M., Ullah, I., Bibi, A., Naeem, M., Singh, M., and Singh, D. (2020). Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm. Electronics, 9.","DOI":"10.3390\/electronics9030406"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s10922-017-9408-1","article-title":"COVE: Co-operative Virtual Network Embedding for Network Virtualization","volume":"26","author":"Feng","year":"2017","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.jss.2018.04.033","article-title":"A novel dynamic resource adjustment architecture for virtual tenant networks in SDN","volume":"143","author":"Ma","year":"2018","journal-title":"J. Syst. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jnca.2018.04.003","article-title":"Performance modeling and comparison of NFV integrated with SDN: Under or aside?","volume":"113","author":"Fahmin","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ullah, W., Hussain, I., Shehzadi, I., Rahman, Z., and Uthansakul, P. (2020). Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment. Sensors, 20.","DOI":"10.3390\/s20072127"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kundimana, G., Vyukusenge, A., and Tsym, A. (2021). Networks Modernization Using SDN and NFV Technologies. Proceedings of the 2021 Systems of Signals Generating and Processing in the Field of on Board Communications, Moscow, Russia, 16\u201318 March 2021, IEEE.","DOI":"10.1109\/IEEECONF51389.2021.9416015"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"32460","DOI":"10.1109\/ACCESS.2018.2842058","article-title":"Implementation of Multipath Network Virtualization with SDN and NFV","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"59255","DOI":"10.1109\/ACCESS.2021.3073240","article-title":"Enabling Internet of Media Things with Edge-Based Virtual Multimedia Sensors","volume":"9","author":"Battisti","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10723-017-9402-6","article-title":"Construction and Resource Allocation of Cost-Efficient Clustered Virtual Network in Software Defined Networks","volume":"15","author":"Li","year":"2017","journal-title":"J. Grid Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3379444","article-title":"A Survey of Network Virtualization Techniques for Internet of Things Using SDN and NFV","volume":"53","author":"Alam","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1109\/JSAC.2020.2986591","article-title":"A Virtual Network Customization Framework for Multicast Services in NFV-Enabled Core Networks","volume":"38","author":"Alhussein","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115839","DOI":"10.1109\/ACCESS.2021.3105944","article-title":"SDN-Enabled Resource Orchestration for Industrial IoT in Collaborative Edge-Cloud Networks","volume":"9","author":"Okwuibe","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.csi.2017.01.001","article-title":"Optimal virtualized network function allocation for an SDN enabled cloud","volume":"54","author":"Leivadeas","year":"2017","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dake, D.K., Gadze, J.D., Klogo, G.S., and Nunoo-Mensah, H. (2021). Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention. Technologies, 9.","DOI":"10.3390\/technologies9030044"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Velusamy, G., and Lent, R. (2020). Smart Site Diversity for a High Throughput Satellite System with Software-Defined Networking and a Virtual Network Function. Future Internet, 12.","DOI":"10.3390\/fi12120225"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"160","DOI":"10.22581\/muet1982.2101.15","article-title":"Power Loss Reduction via Distributed Generation System Injected in a Radial Feeder","volume":"40","author":"Hussain","year":"2021","journal-title":"Mehran Univ. Res. J. Eng. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"A207","DOI":"10.1364\/JOCN.9.00A207","article-title":"Highly Available SDN Control of Flexi-Grid Networks with Network Function Virtualization-Enabled Replication","volume":"9","author":"Casellas","year":"2017","journal-title":"J. Opt. Commun. Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/JCN.2017.000064","article-title":"Optimized provisioning of SDN-enabled virtual networks in geo-distributed cloud computing datacenters","volume":"19","author":"Alhazmi","year":"2017","journal-title":"J. Commun. Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Alonso, R.S., Sitt\u00f3n-Candanedo, I., Casado-Vara, R., Prieto, J., and Corchado, J.M. (2020). Deep Reinforcement Learning for the Management of Software-Defined Networks and Network Function Virtualization in an Edge-IoT Architecture. Sustainability, 12.","DOI":"10.3390\/su12145706"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.comcom.2018.07.017","article-title":"Multi-objective embedding of software-defined virtual networks","volume":"129","author":"Haghani","year":"2018","journal-title":"Comput. Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/TNET.2020.2985908","article-title":"Looking Glass of NFV: Inferring the Structure and State of NFV Network From External Observations","volume":"28","author":"Lin","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"108151","DOI":"10.1016\/j.comnet.2021.108151","article-title":"Software-defined networks for resource allocation in cloud computing: A survey","volume":"195","author":"Mohamed","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00427-9","article-title":"A survey on bandwidth-aware geo-distributed frameworks for big-data analytics","volume":"8","author":"Bergui","year":"2021","journal-title":"J. Big Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3172866","article-title":"Integrated NFV\/SDN architectures: A systematic literature review","volume":"51","author":"Bonfim","year":"2019","journal-title":"ACM Comput. Surv. CSUR"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.future.2018.08.050","article-title":"Implementation of a real-time network traffic monitoring service with network functions virtualization","volume":"93","author":"Yang","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jnca.2019.01.010","article-title":"Performance modeling and analysis of TCP and UDP flows over software defined networks","volume":"130","author":"Lai","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jnca.2019.01.019","article-title":"The application of Software Defined Networking on securing computer networks: A survey","volume":"131","author":"Sahay","year":"2019","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kholidy, H.A. (2022). Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method. Sensors, 22.","DOI":"10.3390\/s22010009"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Babbar, H., Rani, S., Singh, A., Abd-Elnaby, M., and Choi, B.J. (2021). Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking. Sustainability, 13.","DOI":"10.3390\/su13168910"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Shubbar, R., Alhisnawi, M., Abdulhassan, A., and Ahamdi, M. (2021). A Comprehensive Survey on Software-Defined Network Controllers. Next Gener. Internet Things, 199\u2013231.","DOI":"10.1007\/978-981-16-0666-3_18"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Agghey, A.Z., Mwinuka, L.J., Pandhare, S.M., Dida, M.A., and Ndibwile, J.D. (2021). Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach. Symmetry, 13.","DOI":"10.3390\/sym13112192"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dey, S.K., and Rahman, M. (2019). Effects of Machine Learning Approach in Flow-Based Anomaly Detection on Software-Defined Networking. Symmetry, 12.","DOI":"10.20944\/preprints201911.0113.v1"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2743","DOI":"10.1007\/s11277-020-08042-2","article-title":"Improved Network Monitoring Using Software-Defined Networking for DDoS Detection and Mitigation Evaluation","volume":"116","author":"Ramprasath","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108362","DOI":"10.1016\/j.comnet.2021.108362","article-title":"Learning-based hybrid routing for scalability in software defined networks","volume":"198","author":"Nayyer","year":"2021","journal-title":"Comput. Netw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/2534169.2486019","article-title":"B4: Experience with a globally-deployed software defined WAN","volume":"43","author":"Jain","year":"2013","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zaman, S., Khan, L.U., Hussain, I., and Mihet-Popa, L. (2022). Fast Computation of Highly Oscillatory ODE Problems: Applications in High-Frequency Communication Circuits. Symmetry, 14.","DOI":"10.3390\/sym14010115"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Contreras-Valdes, A., Amezquita-Sanchez, J.P., Granados-Lieberman, D., and Valtierra-Rodriguez, M. (2020). Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review. Appl. Sci., 10.","DOI":"10.3390\/app10030950"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep Learning for Land Use and Land Cover Classification based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_52","first-page":"519","article-title":"Multi-dimensional Bayesian network classifiers: A survey","volume":"54","author":"Bielza","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jensen, F.V., and Nielsen, T.D. (2007). Bayesian Networks and Decision Graphs, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-68282-2"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15568318.2020.1827318","article-title":"Frequent-pattern growth algorithm based association rule mining method of public transport travel stability","volume":"15","author":"Hu","year":"2021","journal-title":"Int. J. Sustain. Transp."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ruan, S., Chen, B., Song, K., and Li, H. (2021). Weighted na\u00efve Bayes text classification algorithm based on improved distance correlation coefficient. Neural Comput. Appl., 1\u201310.","DOI":"10.1007\/s00521-021-05989-6"},{"key":"ref_56","unstructured":"Michalski, R.S., Carbonell, J.G., and Mitchell, T.M. (2013). Machine Learning: An Artificial Intelligence Approach, Springer Science & Business Media."},{"key":"ref_57","first-page":"65","article-title":"Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0","volume":"9","author":"Febriantono","year":"2020","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/S0020-7373(87)80076-7","article-title":"A decision-table-based processor for checking completeness and consistency in rule-based expert systems","volume":"26","author":"Cragun","year":"1987","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_59","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2005). Mining Data: Practical Machine Learning Tools and Techniques, Elsevier."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Arnold, J.G., Bieger, K., White, M.J., Srinivasan, R., Dunbar, J.A., and Allen, P.M. (2018). Use of Decision Tables to Simulate Management in SWAT+. Water, 10.","DOI":"10.20944\/preprints201805.0156.v1"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised Machine Learning: A Brief Primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav. Ther."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"e4237","DOI":"10.1002\/dac.4237","article-title":"Stateful firewall-enabled software-defined network with distributed controllers: A network performance study","volume":"32","author":"Prabakaran","year":"2019","journal-title":"Int. J. Commun. Syst."},{"key":"ref_63","first-page":"312","article-title":"Software Defined Network: Load Balancing Algorithm Design and Analysis","volume":"18","author":"Prabakaran","year":"2021","journal-title":"Int. Arab. J. Inf. Technol."},{"key":"ref_64","first-page":"8337","article-title":"Flow based proactive prediction load balancing in stateful firewall enabled software defined network with distributed controllers","volume":"10","author":"Senthil","year":"2021","journal-title":"J. Green Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/709\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:03:10Z","timestamp":1760133790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,18]]},"references-count":64,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030709"],"URL":"https:\/\/doi.org\/10.3390\/s22030709","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,18]]}}}