{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T05:13:06Z","timestamp":1778044386234,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Netw Syst Manage"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10922-025-09989-y","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T06:19:57Z","timestamp":1761545997000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Robustness of DL-Based AAD in SDN-IoT Networks Against OOD Data and Poisoning Attacks Using Autoencoders"],"prefix":"10.1007","volume":"34","author":[{"given":"Tharindu Lakshan","family":"Yasarathna","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nhien-An","family":"Le-Khac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"issue":"2","key":"9989_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10922-024-09803-1","volume":"32","author":"AM Ansari","year":"2024","unstructured":"Ansari, A.M., Nazir, M., Mustafa, K.: Smart homes app vulnerabilities, threats, and solutions: A systematic literature review. J. Netw. Syst. Manage. 32(2), 29 (2024)","journal-title":"J. Netw. Syst. Manage."},{"issue":"11","key":"9989_CR2","doi-asserted-by":"publisher","first-page":"9012","DOI":"10.1109\/JIOT.2021.3120197","volume":"9","author":"Z Bao","year":"2021","unstructured":"Bao, Z., et al.: Threat of adversarial attacks on dl-based IoT device identification. IEEE Internet Things J. 9(11), 9012\u20139024 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"4","key":"9989_CR3","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10922-024-09873-1","volume":"32","author":"M Chemmakha","year":"2024","unstructured":"Chemmakha, M., Habibi, O., Lazaar, M.: Towards a deep learning approach for IoT attack detection based on a new generative adversarial network architecture and gated recurrent unit. J. Netw. Syst. Manage. 32(4), 96 (2024)","journal-title":"J. Netw. Syst. Manage."},{"key":"9989_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102002","volume":"101","author":"IA Khan","year":"2024","unstructured":"Khan, I.A., Razzak, I., Pi, D., Khan, N., Hussain, Y., Li, B., Kousar, T.: Fed-inforce-fusion: A federated reinforcement-based fusion model for security and privacy protection of iomt networks against cyber-attacks. Inf. Fusion 101, 102002 (2024)","journal-title":"Inf. Fusion"},{"issue":"2","key":"9989_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/ajrcos\/2021\/v9i230216","volume":"9","author":"SH Haji","year":"2021","unstructured":"Haji, S.H., et al.: Comparison of software defined networking with traditional networking. Asian J. Res. Comput. Sci. 9(2), 1\u201318 (2021)","journal-title":"Asian J. Res. Comput. Sci."},{"issue":"20","key":"9989_CR6","doi-asserted-by":"publisher","first-page":"7896","DOI":"10.3390\/s22207896","volume":"22","author":"N Ahmed","year":"2022","unstructured":"Ahmed, N., et al.: Network threat detection using machine\/deep learning in sdn-based platforms: A comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction. Sensors 22(20), 7896 (2022)","journal-title":"Sensors"},{"issue":"3","key":"9989_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s10922-023-09749-w","volume":"31","author":"M Cherian","year":"2023","unstructured":"Cherian, M., Varma, S.L.: Secure sdn-iot framework for ddos attack detection using deep learning and counter based approach. J. Netw. Syst. Manage. 31(3), 54 (2023)","journal-title":"J. Netw. Syst. Manage."},{"issue":"8","key":"9989_CR8","doi-asserted-by":"publisher","first-page":"918","DOI":"10.3390\/electronics10080918","volume":"10","author":"D Javeed","year":"2021","unstructured":"Javeed, D., Gao, T., Khan, M.T.: Sdn-enabled hybrid dl-driven framework for the detection of emerging cyber threats in IoT. Electronics 10(8), 918 (2021)","journal-title":"Electronics"},{"key":"9989_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379","volume":"40","author":"S Dong","year":"2021","unstructured":"Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100379 (2021)","journal-title":"Comput. Sci. Rev."},{"issue":"6","key":"9989_CR10","doi-asserted-by":"publisher","first-page":"3228","DOI":"10.1109\/JBHI.2024.3352013","volume":"28","author":"IA Khan","year":"2024","unstructured":"Khan, I.A., Razzak, I., Pi, D., Zia, U., Kamal, S., Hussain, Y.: A novel collaborative sru network with dynamic behaviour aggregation, reduced communication overhead and explainable features. IEEE J. Biomed. Health Inform. 28(6), 3228\u20133235 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"9989_CR11","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s10922-024-09871-3","volume":"32","author":"N Ben Henda","year":"2024","unstructured":"Ben Henda, N., Msolli, A., Haggui, I., Helali, A., Maaref, H.: Attack detection in IoT network using support vector machine and improved feature selection technique. J. Netw. Syst. Manage. 32(4), 92 (2024)","journal-title":"J. Netw. Syst. Manage."},{"issue":"1","key":"9989_CR12","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/info14010041","volume":"14","author":"R Chaganti","year":"2023","unstructured":"Chaganti, R., Suliman, W., Ravi, V., Dua, A.: Deep learning approach for sdn-enabled intrusion detection system in IoT networks. Information 14(1), 41 (2023)","journal-title":"Information"},{"issue":"3","key":"9989_CR13","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/s10922-024-09829-5","volume":"32","author":"R Aljohani","year":"2024","unstructured":"Aljohani, R., Bushnag, A., Alessa, A.: Ai-based intrusion detection for a secure internet of things (IoT). J. Netw. Syst. Manage. 32(3), 56 (2024)","journal-title":"J. Netw. Syst. Manage."},{"key":"9989_CR14","doi-asserted-by":"crossref","unstructured":"Hariharan, A., Gupta, A., Pal, T.: Camlpad: Cybersecurity autonomous machine learning platform for anomaly detection. In: Advances in information and communication: Proceedings of the 2020 future of information and communication conference (FICC), Volume 2, pp. 705\u2013720 (2020). Springer","DOI":"10.1007\/978-3-030-39442-4_52"},{"issue":"7","key":"9989_CR15","doi-asserted-by":"publisher","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","volume":"7","author":"AA Cook","year":"2019","unstructured":"Cook, A.A., M\u0131s\u0131rl\u0131, G., Fan, Z.: Anomaly detection for IoT time-series data: A survey. IEEE Internet Things J. 7(7), 6481\u20136494 (2019)","journal-title":"IEEE Internet Things J."},{"key":"9989_CR16","doi-asserted-by":"crossref","unstructured":"Lakshan\u00a0Yasarathna, T., Le-Khac, N.-A.: Advancing security in sdn-iot networks: Dl-based autonomous anomaly detection with enhanced cross-validation for poisoning attack detection. In: Conference on information technology and its applications, pp. 511\u2013522 (2024). Springer","DOI":"10.1007\/978-3-031-74127-2_41"},{"issue":"13","key":"9989_CR17","doi-asserted-by":"publisher","first-page":"10327","DOI":"10.1109\/JIOT.2020.3048038","volume":"8","author":"H Qiu","year":"2020","unstructured":"Qiu, H., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet Things J. 8(13), 10327\u201310335 (2020)","journal-title":"IEEE Internet Things J."},{"key":"9989_CR18","doi-asserted-by":"publisher","first-page":"132330","DOI":"10.1109\/ACCESS.2020.3010274","volume":"8","author":"S Bulusu","year":"2020","unstructured":"Bulusu, S., Kailkhura, B., Li, B., Varshney, P.K., Song, D.: Anomalous example detection in deep learning: A survey. IEEE Access 8, 132330\u2013132347 (2020)","journal-title":"IEEE Access"},{"key":"9989_CR19","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: A survey. Int. J. Comput. Vis., 1\u201328 (2024)"},{"issue":"3","key":"9989_CR20","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1109\/TNET.2021.3137084","volume":"30","author":"C Zhang","year":"2022","unstructured":"Zhang, C., Costa-Perez, X., Patras, P.: Adversarial attacks against deep learning-based network intrusion detection systems and defense mechanisms. IEEE\/ACM Trans. Netw. 30(3), 1294\u20131311 (2022)","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"1","key":"9989_CR21","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/COMST.2022.3233793","volume":"25","author":"K He","year":"2023","unstructured":"He, K., Kim, D.D., Asghar, M.R.: Adversarial machine learning for network intrusion detection systems: A comprehensive survey. IEEE Commun. Surveys Tutorials 25(1), 538\u2013566 (2023)","journal-title":"IEEE Commun. Surveys Tutorials"},{"issue":"4","key":"9989_CR22","doi-asserted-by":"publisher","first-page":"4197","DOI":"10.1109\/TNSM.2021.3120804","volume":"18","author":"S Dong","year":"2021","unstructured":"Dong, S., Xia, Y., Peng, T.: Network abnormal traffic detection model based on semi-supervised deep reinforcement learning. IEEE Trans. Netw. Serv. Manage. 18(4), 4197\u20134212 (2021)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"9989_CR23","doi-asserted-by":"crossref","unstructured":"Koh, P.W., Steinhardt, J., Liang, P.: Stronger data poisoning attacks break data sanitization defenses. Mach. Learn., 1\u201347 (2022)","DOI":"10.1007\/s10994-021-06119-y"},{"issue":"9","key":"9989_CR24","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1109\/TSE.2021.3087402","volume":"48","author":"S Chakraborty","year":"2021","unstructured":"Chakraborty, S., Krishna, R., Ding, Y., Ray, B.: Deep learning based vulnerability detection: Are we there yet? IEEE Trans. Softw. Eng. 48(9), 3280\u20133296 (2021)","journal-title":"IEEE Trans. Softw. Eng."},{"key":"9989_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100851","volume":"23","author":"X-H Nguyen","year":"2023","unstructured":"Nguyen, X.-H., Le, K.-H.: Robust detection of unknown dos\/ddos attacks in IoT networks using a hybrid learning model. Internet of Things 23, 100851 (2023)","journal-title":"Internet of Things"},{"key":"9989_CR26","first-page":"7068","volume":"34","author":"S Fort","year":"2021","unstructured":"Fort, S., et al.: Exploring the limits of out-of-distribution detection. Adv. Neural. Inf. Process. Syst. 34, 7068\u20137081 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9989_CR27","doi-asserted-by":"crossref","unstructured":"Hore, S., et al.: A sequential deep learning framework for a robust and resilient network intrusion detection system. Comput. Secur., 103928 (2024)","DOI":"10.1016\/j.cose.2024.103928"},{"issue":"5","key":"9989_CR28","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/TETCI.2023.3281833","volume":"7","author":"L Shu","year":"2023","unstructured":"Shu, L., Dong, S., Su, H., Huang, J.: Android malware detection methods based on convolutional neural network: A survey. IEEE Trans. Emerg. Topics Comput. Intell. 7(5), 1330\u20131350 (2023)","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"issue":"19","key":"9989_CR29","doi-asserted-by":"publisher","first-page":"14469","DOI":"10.1007\/s00500-023-09037-4","volume":"27","author":"H Xu","year":"2023","unstructured":"Xu, H., Sun, Z., Cao, Y., Bilal, H.: A data-driven approach for intrusion and anomaly detection using automated machine learning for the internet of things. Soft. Comput. 27(19), 14469\u201314481 (2023)","journal-title":"Soft. Comput."},{"key":"9989_CR30","doi-asserted-by":"crossref","unstructured":"Khan, I.A., Pi, D., Kamal, S., Alsuhaibani, M., Alshammari, B.M.: Federated-boosting: a distributed and dynamic boosting-powered cyber-attack detection scheme for security and privacy of consumer IoT. IEEE Trans. Consumer Electron. (2024)","DOI":"10.1109\/TCE.2024.3499942"},{"issue":"2","key":"9989_CR31","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/s10922-025-09904-5","volume":"33","author":"B Khanal","year":"2025","unstructured":"Khanal, B., Kumar, C., Ansari, M.S.A.: Real-time anomaly detection framework to mitigate emerging threats in software defined networks. J. Netw. Syst. Manage. 33(2), 26 (2025)","journal-title":"J. Netw. Syst. Manage."},{"key":"9989_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109545","volume":"119","author":"F Wahab","year":"2024","unstructured":"Wahab, F., et al.: An sdn-based hybrid-dl-driven cognitive intrusion detection system for IoT ecosystem. Comput. Electr. Eng. 119, 109545 (2024)","journal-title":"Comput. Electr. Eng."},{"key":"9989_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2024.101397","volume":"28","author":"PT Duy","year":"2024","unstructured":"Duy, P.T., Luong, T.D., Quyen, N.H., Pham, V.-H., et al.: Fed-evolver: An automated evolving approach for federated intrusion detection system using adversarial autoencoder in sdn-enabled networks. Internet of Things 28, 101397 (2024)","journal-title":"Internet of Things"},{"key":"9989_CR34","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"9989_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110173","volume":"137","author":"H Mohammadian","year":"2023","unstructured":"Mohammadian, H., Ghorbani, A.A., Lashkari, A.H.: A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems. Appl. Soft Comput. 137, 110173 (2023)","journal-title":"Appl. Soft Comput."},{"key":"9989_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103483","volume":"135","author":"A Sar\u0131kaya","year":"2023","unstructured":"Sar\u0131kaya, A., et al.: Raids: robust autoencoder-based intrusion detection system model against adversarial attacks. Comput. Secur. 135, 103483 (2023)","journal-title":"Comput. Secur."},{"key":"9989_CR37","doi-asserted-by":"crossref","unstructured":"Alrayes, F.S., Zakariah, M., Amin, S.U., Khan, Z.I., Helal, M.: Intrusion detection in IoT systems using denoising autoencoder. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3451726"},{"issue":"1","key":"9989_CR38","doi-asserted-by":"publisher","first-page":"2131056","DOI":"10.1080\/08839514.2022.2131056","volume":"36","author":"F Cai","year":"2022","unstructured":"Cai, F., et al.: Variational autoencoder for classification and regression for out-of-distribution detection in learning-enabled cyber-physical systems. Appl. Artif. Intell. 36(1), 2131056 (2022)","journal-title":"Appl. Artif. Intell."},{"key":"9989_CR39","doi-asserted-by":"crossref","unstructured":"Robertson, C., et al.: Resource-adaptive and ood-robust inference of deep neural networks on iot devices. CAAI Trans. Intell. Technol. (2024)","DOI":"10.1049\/cit2.12384"},{"issue":"23","key":"9989_CR40","doi-asserted-by":"publisher","first-page":"12930","DOI":"10.3390\/app132312930","volume":"13","author":"D Luo","year":"2023","unstructured":"Luo, D., Zhou, H., Bae, J., Yun, B.: Combining contrastive learning with auto-encoder for out-of-distribution detection. Appl. Sci. 13(23), 12930 (2023)","journal-title":"Appl. Sci."},{"key":"9989_CR41","doi-asserted-by":"crossref","unstructured":"Graham, M.S., et al.: Denoising diffusion models for out-of-distribution detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 2948\u20132957 (2023)","DOI":"10.1109\/CVPRW59228.2023.00296"},{"key":"9989_CR42","unstructured":"Hong, Z., et al.: Out-of-distribution detection in medical image analysis: A survey. arXiv preprint arXiv:2404.18279 (2024)"},{"key":"9989_CR43","doi-asserted-by":"publisher","first-page":"22351","DOI":"10.1109\/ACCESS.2021.3056614","volume":"9","author":"ZK Maseer","year":"2021","unstructured":"Maseer, Z.K., Yusof, R., Bahaman, N., Mostafa, S.A., Foozy, C.F.M.: Benchmarking of machine learning for anomaly based intrusion detection systems in the cicids2017 dataset. IEEE Access 9, 22351\u201322370 (2021)","journal-title":"IEEE Access"},{"key":"9989_CR44","doi-asserted-by":"publisher","first-page":"165263","DOI":"10.1109\/ACCESS.2020.3022633","volume":"8","author":"MS Elsayed","year":"2020","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Jurcut, A.D.: Insdn: A novel sdn intrusion dataset. IEEE Access 8, 165263\u2013165284 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3022633","journal-title":"IEEE Access"},{"issue":"13","key":"9989_CR45","doi-asserted-by":"publisher","first-page":"5941","DOI":"10.3390\/s23135941","volume":"23","author":"ECP Neto","year":"2023","unstructured":"Neto, E.C.P., et al.: Ciciot 2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors 23(13), 5941 (2023)","journal-title":"Sensors"},{"key":"9989_CR46","unstructured":"Weng, L.: What are diffusion models? lilianweng. github. io, 21 (2021)"},{"key":"9989_CR47","unstructured":"Xu, L., et al.: Synthesizing tabular data using conditional gan. PhD thesis, Massachusetts Institute of Technology (2020)"},{"key":"9989_CR48","unstructured":"Nicolae, M.-I., et al.: Adversarial robustness toolbox v1. 0.0. arXiv preprint arXiv:1807.01069 (2018)"},{"key":"9989_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109403","volume":"219","author":"LV Ruchel","year":"2022","unstructured":"Ruchel, L.V., Turchetti, R.C., Camargo, E.T.: Evaluation of the robustness of sdn controllers onos and odl. Comput. Netw. 219, 109403 (2022)","journal-title":"Comput. Netw."},{"key":"9989_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105730","volume":"119","author":"M Carletti","year":"2023","unstructured":"Carletti, M., Terzi, M., Susto, G.A.: Interpretable anomaly detection with diffi: Depth-based feature importance of isolation forest. Eng. Appl. Artif. Intell. 119, 105730 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"9","key":"9989_CR51","doi-asserted-by":"publisher","first-page":"2983","DOI":"10.1109\/LCOMM.2021.3091800","volume":"25","author":"W-H Lee","year":"2021","unstructured":"Lee, W.-H., Ozger, M., Challita, U., Sung, K.W.: Noise learning-based denoising autoencoder. IEEE Commun. Lett. 25(9), 2983\u20132987 (2021)","journal-title":"IEEE Commun. Lett."},{"key":"9989_CR52","doi-asserted-by":"crossref","unstructured":"Kim, D., S, V., NG, B.A., Lee, D.: Class scatter ratio based mahalanobis distance approach for detection of internet of things traffic anomalies. Mobile Netw. Appl. 29(2), 373\u2013384 (2024)","DOI":"10.1007\/s11036-023-02257-w"}],"container-title":["Journal of Network and Systems Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-09989-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10922-025-09989-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10922-025-09989-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T04:40:22Z","timestamp":1778042422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10922-025-09989-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["9989"],"URL":"https:\/\/doi.org\/10.1007\/s10922-025-09989-y","relation":{},"ISSN":["1064-7570","1573-7705"],"issn-type":[{"value":"1064-7570","type":"print"},{"value":"1573-7705","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"31 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"16"}}