{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:23:02Z","timestamp":1760239382823,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T00:00:00Z","timestamp":1604448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010193","name":"Korea Electric Power Corporation","doi-asserted-by":"publisher","award":["18A-013"],"award-info":[{"award-number":["18A-013"]}],"id":[{"id":"10.13039\/501100010193","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Owing to ad hoc wireless networks\u2019 properties, the implementation of complex security systems with higher computing resources seems troublesome in most situations. Therefore, the usage of anomaly or intrusion detection systems has attracted considerable attention. The detection systems are implemented either as host-based, run by each node; or as cluster\/network-based, run by cluster head. These two implementations exhibit benefits and drawbacks, such as when cluster-based is used alone, it faces maintaining protection when nodes delay to elect or replace a cluster head. Despite different heuristic approaches that have been proposed, there is still room for improvement. This work proposes a detection system that can run either as host- or as cluster-based to detect routing misbehavior attacks. The detection runs on a dataset built using the proposed routing-information-sharing algorithms. The detection system learns from shared routing information and uses supervised learning, when previous network status or an exploratory network is available, to train the model, or it uses unsupervised learning. The testbed is extended to evaluate the effects of mobility and network size. The simulation results show promising performance even against limiting factors.<\/jats:p>","DOI":"10.3390\/s20216275","type":"journal-article","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T10:29:00Z","timestamp":1604485740000},"page":"6275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning from Routing Information for Detecting Routing Misbehavior in Ad Hoc Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4204-3554","authenticated-orcid":false,"given":"Robert","family":"Basomingera","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Ajou University, Suwon 16499, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2240-0892","authenticated-orcid":false,"given":"Young-June","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Software and Computer Engineering, Ajou University, Suwon 16499, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/COMST.2019.2922584","article-title":"Intrusion Detection Systems: A Cross-Domain Overview","volume":"21","author":"Tidjon","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"95197","DOI":"10.1109\/ACCESS.2019.2928804","article-title":"BP-AODV: Blackhole Protected AODV Routing Protocol for MANETs Based on Chaotic Map","volume":"7","author":"Diab","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7108","DOI":"10.1109\/TVT.2019.2919681","article-title":"An attack-resistant trust inference model for securing routing in vehicular ad hoc networks","volume":"68","author":"Xia","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_4","unstructured":"Comer, D.E. (2015). Computer Networks and Internets, Pearson. [5th ed.]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.1109\/COMST.2018.2859900","article-title":"A Survey of Secure Routing Protocols in Multi-Hop Cellular Networks","volume":"20","author":"Ramezan","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Marchang, N., Datta, R., and Das, S.K. (2017). A Novel Approach for Efficient Usage of Intrusion Detection System in Mobile Ad Hoc Networks. IEEE Trans. Veh. Technol.","DOI":"10.1109\/TVT.2016.2557808"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, W., Yang, Q., and Geng, Y. (2009, January 18\u201320). A survey of anomaly detection methods in networks. Proceedings of the 2009 International Symposium on Computer Network and Multimedia Technology, Wuhan, China.","DOI":"10.1109\/CNMT.2009.5374676"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/SURV.2013.052213.00046","article-title":"Network anomaly detection: Methods, systems and tools","volume":"16","author":"Bhuyan","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sommer, R., and Paxson, V. (2010, January 16\u201319). Outside the closed world: On using machine learning for network intrusion detection. Proceedings of the 2010 IEEE Symposium on Security and Privacy, Berkeley\/Oakland, CA, USA.","DOI":"10.1109\/SP.2010.25"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gopalakrishnan, S., and Rajesh, A. (2019, January 14\u201315). Cluster based Intrusion Detection System for Mobile Ad-hoc Network. Proceedings of the 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India.","DOI":"10.1109\/ICONSTEM.2019.8918871"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"183532","DOI":"10.1109\/ACCESS.2019.2960367","article-title":"DDoS Detection Mechanism Using Trust-Based Evaluation System in VANET","volume":"7","author":"Poongodi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","first-page":"782","article-title":"Intrusion detection in Mobile Adhoc Networks: Bayesian game formulation","volume":"19","author":"Subba","year":"2016","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jim, L.E., and Chacko, J. (2019, January 17\u201320). Decision Tree based AIS strategy for Intrusion Detection in MANET. Proceedings of the TENCON 2019\u20142019 IEEE Region 10 Conference (TENCON), Kochi, India.","DOI":"10.1109\/TENCON.2019.8929362"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s13638-018-1143-0","article-title":"Identity attack detection system for 802.11-based ad hoc networks","volume":"1","author":"Faisal","year":"2018","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"55013","DOI":"10.1109\/ACCESS.2018.2872115","article-title":"Masquerading attacks detection in mobile ad hoc networks","volume":"6","author":"Abbas","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s11276-016-1439-0","article-title":"A novel support vector machine based intrusion detection system for mobile ad hoc networks","volume":"24","author":"Shams","year":"20017","journal-title":"Wirel. Net."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, Y.A., and Lee, W. (2003, January 31). A cooperative intrusion detection system for ad hoc networks. Proceedings of the 1st ACM Workshop on Security of Ad Hoc and Sensor Networks, Fairfax, VA, USA.","DOI":"10.1145\/986858.986877"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Basomingera, R., and Choi, Y.J. (2019, January 9\u201311). Route Cache Based SVM Classifier for Intrusion Detection of Control Packet Attacks in Mobile Ad-Hoc Networks. Proceedings of the 2019 International Conference on Information Networking (ICOIN), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICOIN.2019.8718169"},{"key":"ref_19","first-page":"1","article-title":"The Literature Survey on Manet, Routing Protocols and Metrics","volume":"15","author":"Kaur","year":"2015","journal-title":"Glob. J. Comput. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saeed, N.H., Abbod, M.F., and Al-Raweshidy, H.S. (2012, January 2\u20135). MANET routing protocols taxonomy. Proceedings of the 2012 International Conference on Future Communication Networks, Baghdad, Iraq.","DOI":"10.1109\/ICFCN.2012.6206854"},{"key":"ref_21","unstructured":"Marsic, I. (2007). Wireless Networks Local and Ad Hoc Networks, Rutgers University."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Johnson, D., Hu, Y., and Maltz, D. (2020, October 28). The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4. RFC 4728 2007. Available online: https:\/\/www.hjp.at\/doc\/rfc\/rfc4728.html.","DOI":"10.17487\/rfc4728"},{"key":"ref_23","unstructured":"Clausen, T., Jacquet, P., Adjih, C., Laouiti, A., Minet, P., and Muhlethaler, P. (2020, October 28). Optimized Link State Routing Protocol (OLSR). RFC 3626. Available online: https:\/\/hal.inria.fr\/inria-00471712\/."},{"key":"ref_24","unstructured":"Nsnam (2020, September 07). Ns3::dsr::Link Struct Reference. Available online: https:\/\/www.nsnam.org\/doxygen\/structns3_1_1dsr_1_1_link.html#details."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/COMST.2018.2885894","article-title":"Routing attacks and mitigation methods for RPL-based Internet of Things","volume":"21","author":"Raoof","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_26","unstructured":"Bhattacharyya, A., Banerjee, A., Bose, D., Saha, H.N., and Bhattacharya, D. (2011). Different types of attacks in Mobile ADHOC Network. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"36807","DOI":"10.1109\/ACCESS.2020.2974381","article-title":"An Efficient and Provably Secure Certificateless Key-Encapsulated Signcryption Scheme for Flying Ad-hoc Network","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1049\/iet-wss.2018.5227","article-title":"Adaptive PSO with optimised firefly algorithms for secure cluster-based routing in wireless sensor networks","volume":"9","author":"Pavani","year":"2019","journal-title":"IET Wirel. Sens. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bhushan, B., and Sahoo, G. (2019, January 29\u201330). A Hybrid Secure and Energy Efficient Cluster Based Intrusion Detection system for Wireless Sensing Environment. Proceedings of the 2019 2nd International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India.","DOI":"10.1109\/ICSPC46172.2019.8976509"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhu, X., and Kong, F. (2015, January 18\u201320). Routing and data security scheme based on double encryption in mobile ad hoc networks. Proceedings of the 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, China.","DOI":"10.1109\/IMCCC.2015.380"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TMC.2018.2828814","article-title":"An evolutionary self-cooperative trust scheme against routing disruptions in MANETs","volume":"18","author":"Cai","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"38847","DOI":"10.1109\/ACCESS.2019.2904909","article-title":"SDR Implementation of a D2D Security Cryptographic Mechanism","volume":"7","author":"Balan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mohsen, Y., Hamdy, M., and Shaaban, E. (2019, January 8\u20139). Key distribution protocol for Identity Hiding in MANETs. Proceedings of the 2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS46948.2019.9014807"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"59282","DOI":"10.1109\/ACCESS.2018.2870477","article-title":"SRCPR: SignReCrypting Proxy Re-Signature in Secure VANET Groups","volume":"6","author":"Kanchan","year":"2018","journal-title":"IEEE Access"},{"key":"ref_35","first-page":"338","article-title":"Detecting Blackhole Attack on AODV-based Mobile Ad Hoc Networks by Dynamic Learning Method","volume":"5","author":"Kurosawa","year":"2007","journal-title":"Int. J. Netw. Secur."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zwane, S., Tarwireyi, P., and Adigun, M. (2019, January 21\u201322). A Flow-based IDS for SDN-enabled Tactical Networks. Proceedings of the 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Vanderbijlpark, South Africa.","DOI":"10.1109\/IMITEC45504.2019.9015900"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"47258","DOI":"10.1109\/ACCESS.2018.2864111","article-title":"On detection of Sybil attack in large-scale VANETs using spider-monkey technique","volume":"6","author":"Iwendi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1109\/TMC.2018.2833849","article-title":"Multi-channel based Sybil attack detection in vehicular ad hoc networks using RSSI","volume":"18","author":"Yao","year":"2019","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4753","DOI":"10.1109\/TWC.2019.2928801","article-title":"Secure Data Communications in Wireless Networks Using Multi-Path Avoidance Routing","volume":"18","author":"Sakai","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"158481","DOI":"10.1109\/ACCESS.2019.2945682","article-title":"Intrusion Prevention System for DDoS Attack on VANET With reCAPTCHA Controller Using Information Based Metrics","volume":"7","author":"Poongodi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1016\/j.adhoc.2013.04.014","article-title":"SVELTE: Real-time intrusion detection in the Internet of Things","volume":"11","author":"Raza","year":"2013","journal-title":"Ad Hoc Net."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"25135","DOI":"10.1109\/ACCESS.2020.2970481","article-title":"Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions","volume":"8","author":"Rahman","year":"2020","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1049\/iet-its.2019.0283","article-title":"Combination of HF set and MCDM for stable clustering in VANETs","volume":"14","author":"Chettibi","year":"2020","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TVT.2019.2956228","article-title":"Novel Fuzzy and Game Theory based Clustering and Decision Making for VANETs","volume":"69","author":"Alsarhan","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TDSC.2009.22","article-title":"Mechanism design-based secure leader election model for intrusion detection in MANET","volume":"8","author":"Mohammed","year":"2011","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_46","unstructured":"Kidston, D., Li, L., Mamun, W.A., and Lutfiyya, H. (2011, January 24\u201328). Cross-layer cluster-based data dissemination for failure detection in MANETs. Proceedings of the 2011 7th International Conference on Network and Service Management, Paris, France."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"128757","DOI":"10.1109\/ACCESS.2020.2974105","article-title":"An Adaptive Relay Selection Scheme for Enhancing Network Stability in VANETs","volume":"8","author":"Zukarnain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/TRO.2017.2705119","article-title":"Concurrent control of mobility and communication in multirobot systems","volume":"33","author":"Stephan","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_49","first-page":"430","article-title":"Survey on KNN and Its Variants","volume":"5","author":"Lambda","year":"2016","journal-title":"Int. J. Adv. Res. Comput. Commun. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1109\/TSMCB.2003.817091","article-title":"Comparing different classifiers for automatic age estimation","volume":"34","author":"Lanitis","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_51","first-page":"605","article-title":"Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background","volume":"3","author":"Imandoust","year":"2013","journal-title":"Int. J. Eng. Res. Appl."},{"key":"ref_52","unstructured":"Li, K., and Malik, J. (2016, January 19\u201324). Fast k-nearest neighbour search via dynamic continuous indexing. Proceedings of the 33nd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient knn classification with different numbers of nearest neighbors","volume":"29","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Neural Net. Learn. Syst."},{"key":"ref_54","unstructured":"Gupta, C., Suggala, A.S., Goyal, A., Simhadri, H.V., Paranjape, B., Kumar, A., Goyal, S., Udupa, R., Varma, M., and Jain, P. (2017, January 6\u201311). Protonn: Compressed and accurate knn for resource-scarce devices. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kausar, N., Belhaouari Samir, B., Abdullah, A., Ahmad, I., and Hussain, M. (2011). A Review of Classification Approaches Using Support Vector Machine in Intrusion Detection. Informatics Engineering and Information Science, Springer.","DOI":"10.1007\/978-3-642-25462-8_3"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"33789","DOI":"10.1109\/ACCESS.2018.2841987","article-title":"Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection","volume":"6","author":"Ahmad","year":"2018","journal-title":"IEEE Access"},{"key":"ref_57","unstructured":"Si, S., Zhang, H., Keerthi, S., Mahajan, D., Dhillon, I., and Hsieh, C.J. (2017, January 6\u201311). Gradient boosted decision trees for high dimensional sparse output. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_58","unstructured":"Chen, H., Zhang, H., Boning, D., and Hsieh, C.J. (2019, January 10\u201315). Robust Decision Trees Against Adversarial Examples. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, J., Thu, H.L.T., and Kim, H. (2016, January 15\u201317). Long short term memory recurrent neural network classifier for intrusion detection. Proceedings of the 2016 International Conference on Platform Technology and Service (PlatCon), Jeju, Korea.","DOI":"10.1109\/PlatCon.2016.7456805"},{"key":"ref_60","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2020, September 28). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_61","unstructured":"Nsnam (2020, April 15). Ns-3 | a Discrete-Event Network Simulator for Internet Systems, 2006\u20132020. Available online: https:\/\/www.nsnam.org\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","unstructured":"Powers, D.M. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, Bioinfo Publications."},{"key":"ref_64","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","unstructured":"Glazer, A., Lindenbaum, M., and Markovitch, S. (2013). q-ocsvm: A q-quantile estimator for high-dimensional distributions. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_66","unstructured":"Lu, Y., Zhong, Y., and Bhargava, B. (2003). Packet Loss in Mobile Ad Hoc Networks, Purdue University, Department of Computer Science. Technical Report."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Kosmanos, D., Pappas, A., Aparicio-Navarro, F.J., Maglaras, L., Janicke, H., Boiten, E., and Argyriou, A. (2019, January 20\u201322). Intrusion detection system for platooning connected autonomous vehicles. Proceedings of the 2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece.","DOI":"10.1109\/SEEDA-CECNSM.2019.8908528"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TDSC.2012.67","article-title":"Surviving attacks in challenged networks","volume":"9","author":"Cucurull","year":"2012","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Alikhany, M., and Abadi, M. (2011, January 23\u201324). A dynamic clustering-based approach for anomaly detection in AODV-based MANETs. Proceedings of the 2011 International Symposium on Computer Networks and Distributed Systems (CNDS), Tehran, Iran.","DOI":"10.1109\/CNDS.2011.5764587"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6275\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:29:10Z","timestamp":1760178550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,4]]},"references-count":69,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216275"],"URL":"https:\/\/doi.org\/10.3390\/s20216275","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,11,4]]}}}