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Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.<\/jats:p>","DOI":"10.3390\/fi12110180","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T10:38:35Z","timestamp":1603708715000},"page":"180","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset"],"prefix":"10.3390","volume":"12","author":[{"given":"Ahmed","family":"Mahfouz","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Memphis, Memphis, TN 38152, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1350-6719","authenticated-orcid":false,"given":"Abdullah","family":"Abuhussein","sequence":"additional","affiliation":[{"name":"Department of Information Systems, St. Cloud State University, St. Cloud, MN 56301, USA"}]},{"given":"Deepak","family":"Venugopal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Memphis, Memphis, TN 38152, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3996-7484","authenticated-orcid":false,"given":"Sajjan","family":"Shiva","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Memphis, Memphis, TN 38152, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1108\/OIR-12-2015-0394","article-title":"Internet attacks and intrusion detection system","volume":"41","author":"Singh","year":"2017","journal-title":"Online Inf. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/21642583.2017.1331768","article-title":"A review of detection approaches for distributed denial of service attacks","volume":"5","author":"Kaur","year":"2017","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_3","unstructured":"Davis, J. (2017). Machine Learning and Feature Engineering for Computer Network Security, Queensland University of Technology."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1109\/COMST.2018.2883147","article-title":"Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey","volume":"21","author":"Pacheco","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O\u2019Reilly Media, Inc."},{"key":"ref_6","first-page":"683","article-title":"Towards Generating Real-life Datasets for Network Intrusion Detection","volume":"17","author":"Bhuyan","year":"2015","journal-title":"Int. J. Netw. Secur."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Deshmukh, D.H., Ghorpade, T., and Padiya, P. (2015, January 15\u201317). Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset. Proceedings of the 2015 International Conference on Communication, Information and Computing Technology (ICCICT), Mumbai, India.","DOI":"10.1109\/ICCICT.2015.7045674"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nehinbe, J.O. (2016, January 1\u20132). A critical evaluation of datasets for investigating IDSs and IPSs researches. Proceedings of the 2011 IEEE 10th International Conference on Cybernetic Intelligent Systems (CIS), London, UK.","DOI":"10.1109\/CIS.2011.6169141"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., and Ghorbani, A.A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. ICISSP, University of New Brunswick.","DOI":"10.5220\/0006639801080116"},{"key":"ref_11","unstructured":"Huang, H., Al-Azzawi, H., and Brani, H. (2014). Network traffic anomaly detection. arXiv."},{"key":"ref_12","unstructured":"Lazarevic, A., Kumar, V., and Srivastava, J. (2020, October 21). Intrusion Detection: A Survey, in Managing Cyber Threats. Available online: https:\/\/www.researchgate.net\/publication\/226650646_Intrusion_Detection_A_Survey."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Azeez, N.A., Bada, T.M., Misra, S., Adewumi, A., Van Der Vyver, C., and Ahuja, R. (2019). Intrusion Detection and Prevention Systems: An Updated Review, Springer Science and Business Media LLC.","DOI":"10.1007\/978-981-32-9949-8_48"},{"key":"ref_14","unstructured":"Yeo, L.H., Che, X., and Lakkaraju, S. (2017). Understanding Modern Intrusion Detection Systems: A Survey. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2432","DOI":"10.1109\/COMST.2017.2707140","article-title":"State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow\u2019s Intelligent Network Traffic Control Systems","volume":"19","author":"Fadlullah","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yuan-Fu, Y. (2019, January 6\u20139). A Deep Learning Model for Identification of Defect Patterns in Semiconductor Wafer Map. Proceedings of the 30th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, USA.","DOI":"10.1109\/ASMC.2019.8791815"},{"key":"ref_18","unstructured":"Claesen, M., and De Moor, B. (2015). Hyperparameter search in machine learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ryu, J., Kantardzic, M., and Walgampaya, C. (2010, January 15\u201317). Ensemble Classifier based on Misclassified Streaming Data. Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria.","DOI":"10.2316\/P.2010.674-048"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Elmomen, A.A., El Din, A.B., and Wahdan, A. (2011). Detecting Abnormal Network Traffic in the Secure Event Management Systems. International Conference on Aerospace Sciences and Aviation Technology, The Military Technical College.","DOI":"10.21608\/asat.2011.23416"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7","DOI":"10.24018\/ejers.2018.3.2.302","article-title":"Smart Devices Threats, Vulnerabilities and Malware Detection Approaches: A Survey","volume":"3","author":"BalaGanesh","year":"2018","journal-title":"Eur. J. Eng. Res. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","article-title":"Neural network ensembles","volume":"12","author":"Hansen","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ho, T.K. (2002). Multiple Classifier Combination: Lessons and Next Steps, World Scientific.","DOI":"10.1142\/9789812778147_0007"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","article-title":"A comparative assessment of ensemble learning for credit scoring","volume":"38","author":"Wang","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_25","unstructured":"Koch, R., Golling, M., and Rodosek, G.D. (2014, January 19\u201322). Towards comparability of intrusion detection systems: New data sets. Proceedings of the TERENA Networking Conference, Dublin, Ireland."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Paxson, V., and Floyd, S. (1997, January 7\u201310). Why we don\u2019t know how to simulate the Internet. Proceedings of the 29th Conference on Winter Simulation, Atlanta, GA, USA.","DOI":"10.1145\/268437.268737"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ghorbani, A.A., Lu, W., and Tavallaee, M. (2009). Network Intrusion Detection and Prevention, Springer Science and Business Media LLC.","DOI":"10.1007\/978-0-387-88771-5"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lee, K.-C., Orten, B., Dasdan, A., and Li, W. (2012, January 12\u201316). Estimating conversion rate in display advertising from past erformance data. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339651"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Beck, J.E., and Woolf, B.P. (2000). High-Level Student Modeling with Machine Learning. Proceedings of the Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/3-540-45108-0_62"},{"key":"ref_30","first-page":"21","article-title":"Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods","volume":"78","author":"Karimi","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"John, G., Kohavi, R., and Pfleger, K. (1994). Irrelevant features and the subset selection problem. Machine Learning: Proceedings of the Eleventh International Conference, Morgan Kaufmann.","DOI":"10.1016\/B978-1-55860-335-6.50023-4"},{"key":"ref_33","unstructured":"Biesiada, J., and Duch, W. (2008). Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, N., De Oliveira, R., Ferreira, E., Shinoda, A.A., and Bhargava, B. (2010, January 4\u20137). Identifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach. Proceedings of the 2010 17th International Conference on Telecommunications, Doha, Qatar.","DOI":"10.1109\/ICTEL.2010.5478852"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chebrolu, S., Abraham, A., and Thomas, J.P. (2004). Hybrid Feature Selection for Modeling Intrusion Detection Systems. Proceedings of the Computer Vision, Springer.","DOI":"10.1007\/978-3-540-30499-9_158"},{"key":"ref_36","first-page":"127","article-title":"Optimizing the feature set of wireless intrusion detection systems","volume":"8","author":"Guennoun","year":"2008","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_37","unstructured":"Talavera, L. (2020, October 21). An Evaluation of Filter and Wrapper Methods for Feature Selection in Categorical Clustering. Available online: https:\/\/www.cs.upc.edu\/~talavera\/_downloads\/ida05fs.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.asoc.2016.01.044","article-title":"A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy","volume":"43","author":"Moradi","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/j.ipm.2010.11.007","article-title":"Combining integrated sampling with SVM ensembles for learning from imbalanced datasets","volume":"47","author":"Liu","year":"2011","journal-title":"Inf. Process. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/9704672","article-title":"Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection","volume":"2018","author":"Seo","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_41","first-page":"489","article-title":"An effective over-sampling method for imbalanced data sets classification","volume":"20","author":"Zhai","year":"2011","journal-title":"Chin. J. Electron."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5718","DOI":"10.1016\/j.eswa.2008.06.108","article-title":"Cluster-based under-sampling approaches for imbalanced data distributions","volume":"36","author":"Yen","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hasanin, T., Khoshgoftaar, T.M., Leevy, J.L., and Seliya, N. (2019, January 4\u20139). Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA.","DOI":"10.1109\/BigDataService.2019.00063"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Javaid, A., Niyaz, Q., Sun, W., and Alam, M. (2016, January 3\u20135). A Deep Learning Approach for Network Intrusion Detection System. Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (Formerly BIONETICS), New York, NY, USA.","DOI":"10.4108\/eai.3-12-2015.2262516"},{"key":"ref_46","first-page":"446","article-title":"A study on NSL-KDD dataset for intrusion detection system based on classification algorithms","volume":"4","author":"Dhanabal","year":"2015","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_47","unstructured":"Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C., and Atkinson, R. (2017). Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_49","first-page":"11465","article-title":"Machine learning approach for attack prediction and classification using supervised learning algorithms","volume":"1","author":"MeeraGandhi","year":"2010","journal-title":"Int. J. Comput. Sci. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Nguyen, H.A., and Choi, D. (2008). Application of Data Mining to Network Intrusion Detection: Classifier Selection Model. Proceedings of the Computer Vision, Springer.","DOI":"10.1007\/978-3-540-88623-5_41"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7","DOI":"10.21174\/jomi.v2i1.99","article-title":"Real Time Call Monitoring System Using Spark Streaming and Network Intrusion Detection Using Distributed WekaSpark","volume":"2","author":"Darshan","year":"2017","journal-title":"J. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.procs.2016.06.016","article-title":"Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection","volume":"89","author":"Belavagi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-3-319-07353-8_24","article-title":"Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques","volume":"Volume 1","author":"Hota","year":"2014","journal-title":"Advanced Computing, Networking and Informatics"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.cose.2017.06.005","article-title":"A GA-LR wrapper approach for feature selection in network intrusion detection","volume":"70","author":"Khammassi","year":"2017","journal-title":"Comput. Secur."},{"key":"ref_55","first-page":"48","article-title":"Enhanced intrusion detection system using feature selection method and ensemble learning algorithms","volume":"16","author":"Abdullah","year":"2018","journal-title":"Int. J. Comput. Sci. Inf. Secur."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.cose.2004.09.008","article-title":"Feature deduction and ensemble design of intrusion detection systems","volume":"24","author":"Chebrolu","year":"2005","journal-title":"Comput. Secur."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Roli, F., and Kittler, J. (2002). Multiple Classifier Systems: Third International Workshop, MCS 2002, Cagliari, Italy, 24\u201326 June 2002. Proceedings, Springer Science & Business Media.","DOI":"10.1007\/3-540-45428-4"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1016\/j.dss.2006.04.004","article-title":"Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection","volume":"43","author":"Hansen","year":"2007","journal-title":"Decis. Support Syst."},{"key":"ref_59","unstructured":"Koza, J.R., and Poli, R. (2020, October 21). A Genetic Programming Tutorial. Available online: https:\/\/www.researchgate.net\/publication\/2415604_A_Genetic_Programming_Tutorial."},{"key":"ref_60","unstructured":"Srivats, P. (2019, November 11). Ostinato Packet Generator. Available online: https:\/\/ostinato.org."},{"key":"ref_61","unstructured":"Najera-Gutierrez, G., and Ansari, J.A. (2018). Web Penetration Testing with Kali Linux: Explore the Methods and Tools of Ethical Hacking with Kali Linux, Packt Publishing Ltd."},{"key":"ref_62","first-page":"127","article-title":"Analysis of the package dependency on Debian GNU\/Linux","volume":"1","author":"Sousa","year":"2009","journal-title":"J. Comput. Interdiscip. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","article-title":"N-baiot\u2014Network-based detection of iot botnet attacks using deep autoencoders","volume":"17","author":"Meidan","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_64","first-page":"1681","article-title":"Analysis of Brute Force Attacks with Ylmf-pc Signature","volume":"6","author":"Arzhakov","year":"2016","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"58","DOI":"10.4018\/IJESMA.2018040104","article-title":"Taxonomy of Distributed Denial of Service (DDoS) Attacks and Defense Mechanisms in Present Era of Smartphone Devices","volume":"10","author":"Sharma","year":"2018","journal-title":"Int. J. E Serv. Mob. Appl."},{"key":"ref_66","unstructured":"Kirda, E. (2019, January 16\u201318). Getting Under Alexa\u2019s Umbrella: Infiltration Attacks Against Internet Top Domain Lists. Proceedings of the Information Security: 22nd International Conference (ISC 2019), New York, NY, USA."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Yan, G., Brown, N., and Kong, D. (2013). Exploring discriminatory features for automated malware classification. International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, Springer.","DOI":"10.1007\/978-3-642-39235-1_3"},{"key":"ref_68","unstructured":"Lawrence, D. (2020, October 21). The Hunt for the Financial Industry\u2019s Mostwanted Hacker. Available online: https:\/\/www.bloomberg.com\/news\/features\/2015-06-18\/the-hunt-for-the-financial-industry-s-most-wanted-hacker."},{"key":"ref_69","unstructured":"Nagpal, B., Sharma, P., Chauhan, N., and Panesar, A. (2015, January 11\u201313). DDoS tools: Classification, analysis and comparison. Proceedings of the 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Goyal, P., and Goyal, A. (2017, January 16\u201317). Comparative study of two most popular packet sniffing tools-Tcpdump and Wireshark. Proceedings of the 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN), Girne, Cyprus.","DOI":"10.1109\/CICN.2017.8319360"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1504\/IJSN.2015.070421","article-title":"Network forensics analysis using Wireshark","volume":"10","author":"Ndatinya","year":"2015","journal-title":"Int. J. Secur. Netw."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., and Ghorbani, A.A. (2016, January 19\u201321). Characterization of encrypted and vpn traffic using time-related. Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP), Rome, Italy.","DOI":"10.5220\/0005740704070414"},{"key":"ref_73","unstructured":"Lashkari, A.H., Draper-Gil, G., Mamun, M.S.I., and Ghorbani, A.A. (2017, January 19\u201321). Characterization of tor traffic using time based features. Proceedings of the ICISSP, Porto, Portugal."},{"key":"ref_74","unstructured":"Mahfouz, A., Abuhussein, A., and Shiva, S. (2020, October 21). GTCS Network Attack Dataset 2020. Available online: https:\/\/www.researchgate.net\/publication\/344478320_GTCS_Network_Attack_Dataset."},{"key":"ref_75","first-page":"1725","article-title":"Performance analysis of different feature selection methods in intrusion detection","volume":"2","author":"Amrita","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Eng. Technol."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/180\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:28:10Z","timestamp":1760178490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/12\/11\/180"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,26]]},"references-count":75,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["fi12110180"],"URL":"https:\/\/doi.org\/10.3390\/fi12110180","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,26]]}}}