{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T01:32:01Z","timestamp":1785893521771,"version":"3.56.0"},"reference-count":170,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T00:00:00Z","timestamp":1689379200000},"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":["Cluster Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s10586-023-04069-9","type":"journal-article","created":{"date-parts":[[2023,7,15]],"date-time":"2023-07-15T14:01:56Z","timestamp":1689429716000},"page":"3089-3112","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning for the security of software-defined networks: a review"],"prefix":"10.1007","volume":"26","author":[{"given":"Roya","family":"Taheri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habib","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Engin","family":"Arslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,15]]},"reference":[{"issue":"3","key":"4069_CR1","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TNSM.2020.3004415","volume":"17","author":"TV Phan","year":"2020","unstructured":"Phan, T.V., Nguyen, T.G., Dao, N.-N., Huong, T.T., Thanh, N.H., Bauschert, T.: Deepguard: efficient anomaly detection in sdn with fine-grained traffic monitoring. IEEE Trans. Netw. Serv. Manage. 17(3), 1349\u20131363 (2020)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"4","key":"4069_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/2534169.2486019","volume":"43","author":"S Jain","year":"2013","unstructured":"Jain, S., Kumar, A., Mandal, S., Ong, J., Poutievski, L., Singh, A., Venkata, S., Wanderer, J., Zhou, J., Zhu, M., et al.: B4: Experience with a globally-deployed software defined wan. ACM SIGCOMM Comput. Commun. Rev. 43(4), 3\u201314 (2013)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"issue":"6","key":"4069_CR3","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/CC.2017.7961368","volume":"14","author":"T Wang","year":"2017","unstructured":"Wang, T., Chen, H.: Sguard: a lightweight sdn safe-guard architecture for dos attacks. China Commun. 14(6), 113\u2013125 (2017)","journal-title":"China Commun."},{"key":"4069_CR4","doi-asserted-by":"crossref","unstructured":"Shin, S., Yegneswaran, Y., Porras, P., Gu, G.: Avant-guard: scalable and vigilant switch flow management in software-defined networks. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, vol. Berlin, Germany, pp. 1\u201310 (2013)","DOI":"10.1145\/2508859.2516684"},{"key":"4069_CR5","doi-asserted-by":"crossref","unstructured":"Dotcenko, S., Vladyko, A., Letenko, I.: A fuzzy logic-based information security management for software-defined networks. Paper presented at: 2014 16th International Conference on Advanced Communication Technology (ICACT), vol. Pyeongchang, South Korea, pp. 1-8 (2014)","DOI":"10.1109\/ICACT.2014.6778942"},{"issue":"3","key":"4069_CR6","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TNET.2020.2983976","volume":"28","author":"S Gao","year":"2020","unstructured":"Gao, S., Peng, Z., Xiao, B., Hu, A., Song, Y., Ren, K.: Detection and mitigation of dos attacks in software defined networks. IEEE Trans. Net. 28(3), 1419\u20131433 (2020)","journal-title":"IEEE Trans. Net."},{"issue":"1","key":"4069_CR7","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1109\/JSAC.2021.3126053","volume":"40","author":"D Tang","year":"2022","unstructured":"Tang, D., Yan, Y., Zhang, S., Chen, J., Qin, Z.: Performance and features: Mitigating the low-rate tcp-targeted dos attack via sdn. IEEE J. Selected Areas of Commun. 40(1), 428\u2013435 (2022)","journal-title":"IEEE J. Selected Areas of Commun."},{"key":"4069_CR8","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, L., Gu, G.: Floodguard: a dos attack prevention extension in software-defined networks. In: 2015 45th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, pp. 239-250 (2015)","DOI":"10.1109\/DSN.2015.27"},{"key":"4069_CR9","doi-asserted-by":"crossref","unstructured":"Zheng, J., Li, Q., Gu, G., Cao, J., Yau, D.\u00a0K.\u00a0Y., Wu, J.: Realtime ddos defense using cots sdn switches via adaptive correlation analysis, IEEE Transactions on Information Forensics and Security, pp. 1838-1834 (2018)","DOI":"10.1109\/TIFS.2018.2805600"},{"issue":"7","key":"4069_CR10","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1109\/LCOMM.2019.2896928","volume":"23","author":"A Alshra\u2019a","year":"2019","unstructured":"Alshra\u2019a, A., Seitz, J.: Using inspector device to stop packet injection attack in sdn. IEEE Commun. Lett. 23(7), 1174\u20131177 (2019)","journal-title":"IEEE Commun. Lett."},{"key":"4069_CR11","doi-asserted-by":"crossref","unstructured":"Tang, T.\u00a0A., Mhamdi, L., McLernon, D., Zaidi, S.\u00a0A.\u00a0R., Ghogho, M.: Deep recurrent neural network for intrusion detection in sdn-based networks. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 202\u2013206 (2018)","DOI":"10.1109\/NETSOFT.2018.8460090"},{"issue":"6","key":"4069_CR12","doi-asserted-by":"publisher","first-page":"4286","DOI":"10.1109\/TII.2021.3133300","volume":"18","author":"B Hu,","year":"2022","unstructured":"Hu, B.:, et al.: A deep one-class intrusion detection scheme in software defined industrial networks. IEEE Trans. Industrial Inform. 18(6), 4286\u20134297 (2022)","journal-title":"IEEE Trans. Industrial Inform."},{"key":"4069_CR13","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/ACCESS.2022.3148134","volume":"10","author":"AH Janabi","year":"2022","unstructured":"Janabi, A.H., Kanakis, T., Johnson, M.: Convolutional neural network based algorithm for early warning proactive system security in software defined networks. IEEE Access 10, 14\u2013301 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNSM.2022.3175710","volume":"19","author":"L Yang","year":"2022","unstructured":"Yang, L., Song, Y., Gao, S., Hu, A., Xiao, B.: Griffin: Real-time network intrusion detection system via ensemble of autoencoder in sdn. IEEE Trans. Network and Service Manag. 19, 1\u201313 (2022)","journal-title":"IEEE Trans. Network and Service Manag."},{"key":"4069_CR15","first-page":"756","volume":"22","author":"MSA Muthanna","year":"2022","unstructured":"Muthanna, M.S.A., Alkanhel, R., Muthanna, A., Rafiq, A., Abdullah, W.A.M.: Towards sdn-enabled, intelligent intrusion detection system for internet of things (iot). IEEE Access. 22, 756\u2013769 (2022)","journal-title":"IEEE Access."},{"key":"4069_CR16","doi-asserted-by":"publisher","first-page":"3136","DOI":"10.1109\/TMM.2019.2920613","volume":"21","author":"Y-F Zhou","year":"2019","unstructured":"Zhou, Y.-F., Jiang, R.-H., Wu, X., He, J.-Y., Weng, S., Peng, Q.: Branchgan: unsupervised mutual image-to-image transfer with a single encoder and dual decoders. IEEE Trans. Multimedia. 21, 3136\u20133150 (2019)","journal-title":"IEEE Trans. Multimedia."},{"key":"4069_CR17","doi-asserted-by":"crossref","unstructured":"Ren, S., an\u00a0Ross\u00a0Girshick, K.\u00a0H., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28 (2017)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"4069_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume":"11045","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman, S.M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. Lect. Notes Comput. Sci. 11045, 3\u201311 (2018)","journal-title":"Lect. Notes Comput. Sci."},{"key":"4069_CR19","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"13","author":"S Roy","year":"2020","unstructured":"Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldati, G., Rota, P., Passerini, A., van Sloun, R.J.G., Ricci, E., Demi, L.: Deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 13, 2676\u20132688 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4069_CR20","doi-asserted-by":"publisher","first-page":"4001","DOI":"10.1109\/TMI.2020.3008930","volume":"13","author":"I Oksuz","year":"2020","unstructured":"Oksuz, I., Clough, J.R., Ruijsink, B., Anton, E.P., Bustin, A., Cruz, G., Prieto, C., King, A.P., Schnabel, J.A.: Deep learning-based detection and correction of cardiac mr motion artefacts during reconstruction for high-quality segmentation\u2019\u2019. IEEE Trans. Med.l Imaging 13, 4001\u20134011 (2020)","journal-title":"IEEE Trans. Med.l Imaging"},{"key":"4069_CR21","first-page":"994","volume":"12","author":"J Yu","year":"2018","unstructured":"Yu, J., Chen, H., Dou, Q., Qin, J., Heng, P.-A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 12, 994\u20131015 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4069_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.autcon.2020.103393","volume":"120","author":"H Ahmed","year":"2020","unstructured":"Ahmed, H., La, H.M., Tran, K.: Rebar detection and localization for bridge deck inspection and evaluation using deep residual network. Automat. Constr. 120, 1\u201338 (2020)","journal-title":"Automat. Constr."},{"key":"4069_CR23","unstructured":"Ahmed, H., Gucunski, N., La, H.\u00a0M.: Rebar detection using ground penetrating radar with state-of-the-art convolutional neural networks,\u201d The 9th International Conference on Structural Health Monitoring of Intelligent infrastructure, pp. 1-6 (2019). [Online]. Available: https:\/\/ara.cse.unr.edu\/wp-content\/uploads\/2014\/12\/SHMII-GPR-Paper-Final-Version-4.pdf [Accessed on 20 June 2022]"},{"key":"4069_CR24","doi-asserted-by":"crossref","unstructured":"Ahmed, H., La, H.\u00a0M., Pekcan, G.: Rebar detection and localization for non-destructive infrastructure evaluation using deep residual networks. Proceedings of the 14th International Symposium on Visual Computing. pp. 1-6 (2019)","DOI":"10.1007\/978-3-030-33720-9_49"},{"key":"4069_CR25","doi-asserted-by":"crossref","unstructured":"Ahmed, H., Tavakolli, A., La, H.\u00a0M.: Use of deep encoder-decoder network for sub-surface inspection and evaluation of bridge decks. Proceedings of the 13th International Workshop on Structural Health Monitoring 2022. p. (Accepted for Publication), (2022)","DOI":"10.12783\/shm2021\/36334"},{"key":"4069_CR26","doi-asserted-by":"crossref","unstructured":"Ahmed, H., Nguyen, S.\u00a0T., La, D., Le, C.\u00a0P., La, H.\u00a0M.: Multi-directional bicycle robot for bridge inspection with steel defect detection system. IEEE International Conference on Robotics and Automation (ICRA) 2022, p. (Accepted for Publication), (2022)","DOI":"10.1109\/IROS47612.2022.9981325"},{"key":"4069_CR27","first-page":"657","volume":"186","author":"S Chen","year":"2019","unstructured":"Chen, S., Lin, H., Yao, M.: Improving the efficiency of encoder-decoder architecture for pixel-level crack detection. IEEE Access. 186, 657\u2013671 (2019)","journal-title":"IEEE Access."},{"key":"4069_CR28","first-page":"1","volume":"14","author":"H Ahmed","year":"2020","unstructured":"Ahmed, H., La, H.M., Gucunski, N.: Review of non-destructive civil infrastructure evaluation for bridges: State-of-the-art robotic platforms, sensors and algorithms. Sensors 14, 1\u201338 (2020)","journal-title":"Sensors"},{"key":"4069_CR29","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/JAS.2020.1003453","volume":"8","author":"I Ahmed","year":"2021","unstructured":"Ahmed, I., Din, S., Jeon, G., Piccialli, F., Fortino, G.: Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning. IEEE\/CAA J. Automatica Sinica. 8, 1253\u20131270 (2021)","journal-title":"IEEE\/CAA J. Automatica Sinica."},{"key":"4069_CR30","doi-asserted-by":"publisher","first-page":"6145","DOI":"10.1109\/LRA.2020.3010461","volume":"5","author":"A Church","year":"2020","unstructured":"Church, A., Lloyd, J., Hadsell, R., Lepora, N.F.: Deep reinforcement learning for tactile robotics: Learning to type on a braille keyboard. IEEE Robotics and Automation Letters. 5, 6145\u20136152 (2020)","journal-title":"IEEE Robotics and Automation Letters."},{"key":"4069_CR31","doi-asserted-by":"publisher","first-page":"3826","DOI":"10.1109\/TCYB.2020.2977374","volume":"50","author":"TT Nguyen","year":"2020","unstructured":"Nguyen, T.T., Nguyen, N.D., Nahavandi, S.: Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications. IEEE Trans. Cybernet. 50, 3826\u20133839 (2020)","journal-title":"IEEE Trans. Cybernet."},{"key":"4069_CR32","first-page":"1393","volume":"21","author":"J X.","year":"2019","unstructured":"X. J. et al.: A survey of machine learning techniques applied to software defined networking (sdn): Research issues and challenges. IEEE Commun. Surveys and Tutorials 21, 1393\u2013430 (2019)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR33","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/ACCESS.2020.3041765","volume":"8","author":"I Ahmad","year":"2020","unstructured":"Ahmad, I., Shahabuddin, S., Malik, H., Harjula, E., Lepp\u00e4nen, T., Loven, L., Anttonen, A., Sodhro, A.H., Alam, M.M., Juntti, M., et al.: Machine learning meets communication networks: current trends and future challenges. IEEE Access 8, 223\u2013418 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR34","first-page":"1","volume":"8","author":"JCC Chica","year":"2020","unstructured":"Chica, J.C.C., Imbachi, J.C., Vega, J.F.B.: Security in sdn: A comprehensive survey. J. Net. Comput. Appl. 8, 1\u201323 (2020)","journal-title":"J. Net. Comput. Appl."},{"key":"4069_CR35","first-page":"016","volume":"122","author":"MB Jimenez","year":"2021","unstructured":"Jimenez, M.B., Fernandez, D., Rivaneira, J.E., Bellido, L., Cardenas, A.: A survey of the main security issues and solutions for the sdn architecture. IEEE Access. 122, 016\u2013039 (2021)","journal-title":"IEEE Access."},{"key":"4069_CR36","first-page":"39","volume":"1","author":"Y Maleh","year":"2022","unstructured":"Maleh, Y., Qasmaoui, Y., El Gholami, K., Sadqi, Y., Mounir, S.: A comprehensive survey on sdn security: threats, mitigations, and future directions. J. Reliable Intell. Environ. 1, 39 (2022)","journal-title":"J. Reliable Intell. Environ."},{"key":"4069_CR37","first-page":"820","volume":"45","author":"M Rahouti","year":"2022","unstructured":"Rahouti, M., Xiong, K., Xin, Y., Jagatheesaperumal, S.K., Ayyash, M., Shaheed, M.: Sdn security review: threat taxonomy, implications, and open challenges. IEEE Access 45, 820\u2013855 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR38","first-page":"1","volume":"5","author":"R Deb","year":"2022","unstructured":"Deb, R., Roy, S.: A comprehensive survey of vulnerability and information security in sdn. Comput. Net. 5, 1\u201330 (2022)","journal-title":"Comput. Net."},{"key":"4069_CR39","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.comcom.2020.02.085","volume":"154","author":"MP Singh","year":"2020","unstructured":"Singh, M.P., Bhandari, A.: New-flow-based ddos attacks in sdn: Taxonomy, rationales and research challenges. Comp. Commun. 154, 509\u2013527 (2020)","journal-title":"Comp. Commun."},{"key":"4069_CR40","first-page":"582","volume":"104","author":"R Amin","year":"2019","unstructured":"Amin, R., Rojas, E., Aqdus, A., Ramzan, S., Casillas-Perez, D., Arco, J.M.: A survey on machine learning techniques for routing optimization in sdn. IEEE Access 104, 582\u2013612 (2019)","journal-title":"IEEE Access"},{"key":"4069_CR41","doi-asserted-by":"publisher","first-page":"3259","DOI":"10.1109\/COMST.2018.2837161","volume":"20","author":"R Amin","year":"2018","unstructured":"Amin, R., Reisslein, M., Shah, N.: Hybrid sdn networks: a survey of existing approaches\u2019\u2019. IEEE Commun. Surveys and Tutorials 20, 3259\u20133307 (2018)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JPROC.2019.2895553","volume":"107","author":"W Kellerer","year":"2019","unstructured":"Kellerer, W., Kalmbach, P., Blenk, A., Basta, A., Reisslein, M., Schmid, S.: Adaptable and data-driven softwarized networks: Review, opportunities, and challenges. Proceedings of the IEEE 107, 1\u201335 (2019)","journal-title":"Proceedings of the IEEE"},{"key":"4069_CR43","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/COMST.2017.2782482","volume":"20","author":"F Bannour","year":"2018","unstructured":"Bannour, F., Souihi, S., Mellouk, A.: Distributed sdn control: survey, taxonomy, and challenges. IEEE Commun Surveys and Tutorials 20, 333\u2013355 (2018)","journal-title":"IEEE Commun Surveys and Tutorials"},{"key":"4069_CR44","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/COMST.2018.2871061","volume":"21","author":"X Huang","year":"2019","unstructured":"Huang, X., Cheng, S., Cao, K., Cong, P., Wei, T., Hu, S.: A survey of deployment solutions and optimization strategies for hybrid sdn networks. IEEE Commun. Surveys and Tutorials 21, 1483\u20131507 (2019)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR45","doi-asserted-by":"publisher","first-page":"107981","DOI":"10.1016\/j.comnet.2021.107981","volume":"192","author":"S Khorsandroo","year":"2021","unstructured":"Khorsandroo, S., Sanchez, A.G., Tosun, A.S., Arco, J., Doriguzzi-Corin, R.: Hybrid sdn evolution: A comprehensive survey of the state-of-the-art. Comput. Net. 192, 107981 (2021)","journal-title":"Comput. Net."},{"key":"4069_CR46","first-page":"028","volume":"91","author":"O Al-Heety","year":"2020","unstructured":"Al-Heety, O., Zakaria, Z., Ismail, M., Shakir, M.M., Alani, S., Alsariera, H.: A comprehensive survey: benefits, services, recent works, challenges, security, and use cases for sdn-vanet. IEEE Access 91, 028\u2013048 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3379444","volume":"53","author":"I Alam","year":"2020","unstructured":"Alam, I., Sharif, K., Li, F., Latif, Z., Karim, M.M., Biswas, S., Nour, B., Wang, Y.: A survey of network virtualization techniques for internet of things using sdn and nfv. ACM Comput. Survey 53, 1\u201340 (2020)","journal-title":"ACM Comput. Survey"},{"key":"4069_CR48","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1109\/COMST.2018.2862350","volume":"21","author":"I Farris","year":"2019","unstructured":"Farris, I., Taleb, T., Khettab, Y., Song, J.: A survey on emerging sdn and nfv security mechanisms for iot systems. IEEE Commun. Surveys and Tutorials 21, 812\u2013838 (2019)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR49","first-page":"109","volume":"8","author":"A Ali","year":"2020","unstructured":"Ali, A., Yousaf, M.M.: Novel three-tier intrusion detection and prevention system in software-defined networks. IEEE Access 8, 109\u2013677 (2020)","journal-title":"IEEE Access"},{"issue":"6","key":"4069_CR50","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1109\/TII.2021.3128581","volume":"18","author":"J Wang","year":"2021","unstructured":"Wang, J., Liu, J., Guo, H., Mao, B.: Deep reinforcement learning for securing software-defined industrial networks with distributed control plane. IEEE Trans. Industr. Inf. 18(6), 4275\u20134285 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4069_CR51","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1109\/TR.2015.2421391","volume":"64","author":"ST Ali","year":"2015","unstructured":"Ali, S.T., Sivaraman, V., Radford, A., Jha, S.: A suvey of securing network using software defined networking. IEEE Trans. Reliab. 64, 1086\u20131098 (2015)","journal-title":"IEEE Trans. Reliab."},{"key":"4069_CR52","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/SURV.2014.012214.00180","volume":"16","author":"BAA Nunes","year":"2014","unstructured":"Nunes, B.A.A., Mendonca, M., Nguyen, X.-N., Obraczka, K., Turletti, T.: A survey of software-defined networking: Past, present, and future of programmable networks. IEEE Commun. Surveys and Tutorials 16, 1617\u20131635 (2014)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR53","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1109\/COMST.2015.2453114","volume":"18","author":"S Scott-Hayward","year":"2016","unstructured":"Scott-Hayward, S., Natarajan, S., Sezer, S.: A survey of security in software defined networks. IEEE Commun. Surveys and Tutorials 18, 623\u2013655 (2016)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR54","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1109\/COMST.2015.2474118","volume":"17","author":"I Ahmad","year":"2015","unstructured":"Ahmad, I., Namal, S., Ylianttila, M., Gurtov, A.: Security in software defined networks: a survey. IEEE Commun. Surveys and Tutorial 17, 2317\u20132347 (2015)","journal-title":"IEEE Commun. Surveys and Tutorial"},{"key":"4069_CR55","doi-asserted-by":"publisher","first-page":"5803","DOI":"10.1002\/sec.1737","volume":"9","author":"K Benzekki","year":"2017","unstructured":"Benzekki, K., El Fergougui, A., Elalaoui, A.E.: Software-defined networking (sdn): a survey. Security and Commun. Net. 9, 5803\u20135833 (2017)","journal-title":"Security and Commun. Net."},{"key":"4069_CR56","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jnca.2016.04.011","volume":"68","author":"W Li","year":"2016","unstructured":"Li, W., Meng, W., Kwok, L.F.: A survey on openflow-based software-defined networks: security challenges and countermeasures. J. Net. Comput. Appl. 68, 126\u2013139 (2016)","journal-title":"J. Net. Comput. Appl."},{"key":"4069_CR57","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/COMST.2015.2487361","volume":"82","author":"Q Yan","year":"2016","unstructured":"Yan, Q., Yu, F.R., Gong, Q., Li, J.: Software-defined networking (sdn) and distributed denial of service (ddos) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Commun. Surveys Tutorials. 82, 602\u2013623 (2016)","journal-title":"IEEE Commun. Surveys Tutorials."},{"key":"4069_CR58","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1109\/COMST.2017.2689819","volume":"19","author":"T Dargahi","year":"2017","unstructured":"Dargahi, T., Alberto Caponi, M.A., Bianchi, G., Conti, M.: A survey on the security of stateful sdn data planes. IEEE Commun. Surveys and Tutorials 19, 1701\u20131726 (2017)","journal-title":"IEEE Commun. Surveys and Tutorials"},{"key":"4069_CR59","first-page":"813","volume":"80","author":"S Dong","year":"2019","unstructured":"Dong, S., Abbas, K., Jain, R.: A survey on distributed denial of service (ddos) attacks in sdn and cloud computing environments. IEEE Access 80, 813\u2013828 (2019)","journal-title":"IEEE Access"},{"key":"4069_CR60","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s12083-017-0630-0","volume":"12","author":"N Sultana","year":"2019","unstructured":"Sultana, N., Chilamkurti, N., Peng, W., Alhadad, R.: Survey on sdn based network intrusion detection system using machine learning approaches. Peer-to-Peer Network. Appl. 12, 493\u2013501 (2019)","journal-title":"Peer-to-Peer Network. Appl."},{"key":"4069_CR61","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Shatabda, S., Islam, A., Robin, M., Islam, T.: et\u00a0al., Intrusion detection system in software-defined networks using machine learning and deep learning techniques\u2013a comprehensive survey. (2021)","DOI":"10.36227\/techrxiv.17153213.v1"},{"key":"4069_CR62","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1007\/s10586-020-03184-1","volume":"24","author":"T Jafarian","year":"2021","unstructured":"Jafarian, T., Masdari, M., Ghaffari, A., Majidzadeh, K.: A survey and classification of the security anomaly detection mechanisms in software defined networks. Cluster Comput. 24, 1235\u20131253 (2021)","journal-title":"Cluster Comput."},{"key":"4069_CR63","first-page":"397","volume":"95","author":"Y Zhao","year":"2019","unstructured":"Zhao, Y., Li, Y., Zhang, X., Geng, G., Zhang, W., Sun, Y.: A survey of networking applications applying the software defined networking concept based on machine learning. IEEE Access 95, 397\u2013418 (2019)","journal-title":"IEEE Access"},{"key":"4069_CR64","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/cpe.5300","volume":"32","author":"T Han","year":"2020","unstructured":"Han, T., Jan, S.R.U., Tan, Z., Usman, M., Jan, M.A., Khan, R., Xu, Y.: A comprehensive survey of security threats and their mitigation techniques for next-generation sdn controllers. Concurrency Computat. Pract. Exper. 32, 1\u201321 (2020)","journal-title":"Concurrency Computat. Pract. Exper."},{"key":"4069_CR65","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"7553","key":"4069_CR66","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"4069_CR67","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420 (2021)","journal-title":"SN Comput. Sci."},{"key":"4069_CR68","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.knosys.2019.105124","volume":"189","author":"A Aldweesh","year":"2020","unstructured":"Aldweesh, A., Derhab, A., Emam, A.Z.: Deep learning approaches for anomaly-based intrusion detection systems: A survey, taxonomy, and open issues. Knowl. Based Syst. 189, 105\u2013124 (2020)","journal-title":"Knowl. Based Syst."},{"key":"4069_CR69","unstructured":"O\u2019Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, (2015)"},{"key":"4069_CR70","unstructured":"Glorot, X., Bengio, Y.:Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, pp. 249-256, (2010)"},{"issue":"5","key":"4069_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3234150","volume":"51","author":"S Pouyanfar","year":"2018","unstructured":"Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.-L., Chen, S.-C., Iyengar, S.S.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surveys (CSUR) 51(5), 1\u201336 (2018)","journal-title":"ACM Comput. Surveys (CSUR)"},{"key":"4069_CR72","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., Valaee, S.: Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078, (2017)"},{"key":"4069_CR73","first-page":"1","volume":"14","author":"J Naskath","year":"2022","unstructured":"Naskath, J., Sivakamasundari, G., Begum, A.: A study on different deep learning algorithms used in deep neural nets: Mlp som and dbn. Wireless Personal Commun. 14, 1\u201324 (2022)","journal-title":"Wireless Personal Commun."},{"key":"4069_CR74","doi-asserted-by":"crossref","unstructured":"Tan1, C., Sun2, F., Kong1, T., Zhang1, W., Yang1, C., Liu, C.: A survey on deep transfer learning. International Conference on Artificial Neural Networks, p. 270-279, (2018)","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"4069_CR75","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.comcom.2020.12.013","volume":"168","author":"X Liu","year":"2021","unstructured":"Liu, X., Yu, W., Liang, F., Griffith, D., Golmie, N.: On deep reinforcement learning security for industrial internet of things. Comput Commun. 168, 20\u201332 (2021)","journal-title":"Comput Commun."},{"key":"4069_CR76","first-page":"34","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Hu, T., Tang, G., Xi, J., Lu, J.: Sgs: safe-guard scheme for protecting control plane against ddos attacks in software-defined networking. IEEE Access 7, 34\u2013699 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"4069_CR77","first-page":"18","volume":"18","author":"J Min","year":"2020","unstructured":"Min, J., Yuejie, S., Qing, G., Zihe, G., Suofe, X.: Ddos attack detection method for space-based network based on sdn architecture. ZTE Commun. 18(4), 18\u201325 (2020)","journal-title":"ZTE Commun."},{"issue":"2","key":"4069_CR78","doi-asserted-by":"publisher","first-page":"923","DOI":"10.32604\/iasc.2022.024668","volume":"33","author":"F Alanazi","year":"2022","unstructured":"Alanazi, F., Jambi, K., Eassa, F., Khemakhem, M., Basuhail, A., Alsubhi, K.: Ensemble deep learning models for mitigating ddos attack in software-defined network. Intell. Automat. Soft Comput. 33(2), 923\u2013938 (2022)","journal-title":"Intell. Automat. Soft Comput."},{"issue":"53","key":"4069_CR79","first-page":"972","volume":"8","author":"S H.,","year":"2021","unstructured":"H., S. et al.: A deep cnn ensemble framework for efficient ddos attack detection in software defined networks. IEEE Access 8(53), 972\u2013983 (2021)","journal-title":"IEEE Access"},{"key":"4069_CR80","first-page":"73","volume":"10","author":"DMB Lent","year":"2022","unstructured":"Lent, D.M.B., Novaes, M.P., Carvalho, L.F., Lloret, J., Rodriguez, J.J.P.C., Proenca, M.L.: A gated recurrent unit deep learning model to detect and mitigate distributed denial of service and portscan attacks. IEEE Access 10, 73\u2013229 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR81","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1016\/j.future.2019.10.015","volume":"111","author":"RMA Ujjan","year":"2020","unstructured":"Ujjan, R.M.A., Pervez, Z., Dahal, K., Bashir, A.K., Mumtaz, R., Gonz\u00e1lez, J.: Towards sflow and adaptive polling sampling for deep learning based ddos detection in sdn. Futur. Gener. Comput. Syst. 111, 763\u2013779 (2020)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"4069_CR82","first-page":"44","volume":"7","author":"S Yeom","year":"2022","unstructured":"Yeom, S., Choi, C., Kim, K.: Lstm-based collaborative source-side ddos attack detection. IEEE Access 7, 44\u2013046 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR83","first-page":"25","volume":"14","author":"JD Gadze","year":"2021","unstructured":"Gadze, J.D., Bamfo-Asante, A.A., Agyemang, J.O., Nunoo-Mensah, H., Opare, K.A.-B.: An investigation into the application of deep learning in the detection and mitigation of ddos attack on sdn controllers. Technologies 14, 25 (2021)","journal-title":"Technologies"},{"key":"4069_CR84","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1109\/TITS.2020.3027390","volume":"22","author":"J Shu","year":"2021","unstructured":"Shu, J., Zhou, L., Zhang, W., Du, X., Guizani, M.: Collaborative intrusion detection for vanets: a deep learning-based distributed sdn approach. IEEE Trans. Intell. Transport. Syst. 22, 4519\u20134523 (2021)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"4069_CR85","doi-asserted-by":"publisher","first-page":"3559","DOI":"10.1109\/JIOT.2020.2973176","volume":"7","author":"N Ravi","year":"2020","unstructured":"Ravi, N., Shalinie, S.M.: Learning-driven detection and mitigation of ddos attack in iot via sdn-cloud architecture. IEEE Int. Things J. 7, 3559\u20133571 (2020)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR86","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TDSC.2021.3118081","volume":"19","author":"A Rezapour","year":"2022","unstructured":"Rezapour, A., Tzeng, W.-G.: Rl-shield: mitigating target link-flooding attacks using sdn and deep reinforcement learning routing algorithm. IEEE Trans. Depend. Secure Comput. 19, 1\u201317 (2022)","journal-title":"IEEE Trans. Depend. Secure Comput."},{"key":"4069_CR87","first-page":"885","volume":"34","author":"R ur Rasool, R., Ashraf, U., Ahmed, K","year":"2019","unstructured":"ur Rasool, R., Ashraf, U., Ahmed, K., Wang, H., Rafique, W., Anwar, Z.: Cyberpulse: a machine learning based link flooding attack mitigation system for software defined networks. IEEE Access 34, 885\u2013900 (2019)","journal-title":"IEEE Access"},{"key":"4069_CR88","doi-asserted-by":"crossref","unstructured":"Ahuja, N., Singal, G., Mukhopadhyay, D.: Dlsdn: Deep learning for ddos attack detection in software defined networking. 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), (2021)","DOI":"10.1109\/Confluence51648.2021.9376879"},{"key":"4069_CR89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JIOT.2022.3232257","volume":"9","author":"J Wang","year":"2022","unstructured":"Wang, J., Liu, J.: Deep learning for securing software-defined industrial internet of things: attacks and countermeasures. IEEE Int. Things J. 9, 1\u201311 (2022)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR90","doi-asserted-by":"crossref","unstructured":"Soltani, S., Shojafar, M., Mostafaeit, H., Pooranian, Z., Tafazolli, R.: Link latency attack in software-defined networks. 17th International Conference on Network and Service Management (CNSM), (2021)","DOI":"10.23919\/CNSM52442.2021.9615598"},{"issue":"10","key":"4069_CR91","doi-asserted-by":"publisher","first-page":"9563","DOI":"10.1109\/JIOT.2020.2984088","volume":"7","author":"J Wang","year":"2020","unstructured":"Wang, J., Tan, Y., Liu, J., Zhang, Y.: Topology poisoning attack in sdn-enabled vehicular edge network. IEEE Int. Things J. 7(10), 9563\u20139575 (2020)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR92","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1109\/TNSM.2017.2701549","volume":"14","author":"R Mohammadi","year":"2017","unstructured":"Mohammadi, R., Javidan, R., Conti, M.: Slicots: an sdn-based lightweight countermeasure for tcp syn flooding attacks. IEEE Trans. Net. Service Manag. 14, 487\u2013498 (2017)","journal-title":"IEEE Trans. Net. Service Manag."},{"key":"4069_CR93","doi-asserted-by":"crossref","unstructured":"Chen, M.-H., Ciou, J.-Y., Chung, I.-H., Chou, C.-F.: Flexprotect: a sdn-based ddos attack protection architecture for multi-tenant data centers.In: Proceedings of International Conference on High Performance Computing Asia-Pacific Region., pp. 1-6, (2018)","DOI":"10.1145\/3149457.3149476"},{"key":"4069_CR94","doi-asserted-by":"crossref","unstructured":"Boite, J., Nardin, P.-A., Rebecchi, F., Bouet, M., Conan, V.: Statesec: stateful monitoring for ddos protection in software defined networks. Paper presented at: 2017 IEEE Conference on Network Softwarization (NetSoft), vol. Bologna, Italy, pp. 1-6, (2017)","DOI":"10.1109\/NETSOFT.2017.8004113"},{"key":"4069_CR95","first-page":"680","volume":"69","author":"JE Varghese","year":"2021","unstructured":"Varghese, J.E., Muniyal, B.: An efficient ids framework for ddos attacks in sdn environment. IEEE Access 69, 680\u2013700 (2021)","journal-title":"IEEE Access"},{"key":"4069_CR96","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/ACCESS.2018.2885164","volume":"7","author":"Y Xu","year":"2019","unstructured":"Xu, Y., Sun, H., aand Shijin Sun, F. X.: Efficient ddos detection based on k-fknn in software defined networks. IEEE Access 7, 160\u2013547 (2019)","journal-title":"IEEE Access"},{"key":"4069_CR97","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/ACCESS.2020.2992044","volume":"8","author":"MP Novaes","year":"2020","unstructured":"Novaes, M.P., Carvalho, L.F., Lloret, J., Proen\u00e7a, M.L.: Long short-term memory and fuzzy logic for anomaly detection and mitigation in software-defined network environment. IEEE Access 8, 83\u2013765 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR98","first-page":"662","volume":"109","author":"J Hussain","year":"2020","unstructured":"Hussain, J., Hnamte, V.: Novel three-tier intrusion detection and prevention system in software defined network. IEEE Access 109, 662\u2013677 (2020)","journal-title":"IEEE Access"},{"issue":"2","key":"4069_CR99","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/2935634.2935636","volume":"46","author":"D Gkounis","year":"2016","unstructured":"Gkounis, D., Kotronis, V., Liaskos, C., Dimitropoulos, X.: On the interplay of link-flooding attacks and traffic engineering. SIGCOMM Comput. Commun. Rev. 46(2), 5\u201311 (2016)","journal-title":"SIGCOMM Comput. Commun. Rev."},{"key":"4069_CR100","unstructured":"Ahuja, N., Singal, G., Mukhopadhyay, D.: Ddos attack sdn dataset,\u201d https:\/\/data.mendeley.com\/datasets\/jxpfjc64kr\/1, 2020"},{"key":"4069_CR101","doi-asserted-by":"crossref","unstructured":"Xiang, S., Zhu, H., Xiao, L., Xie, W.: Modeling and verifying topoguard in openflow-based software defined networks. In: Proceedings of 2018 International Symposium on Theoretical Aspects of Software Engineering (TASE). pp. 84-91, (2018)","DOI":"10.1109\/TASE.2018.00019"},{"key":"4069_CR102","doi-asserted-by":"crossref","unstructured":"Skowyra, R., Xu, L., Gu, G., Dedhia, V., Hobson, T., Okhravi, H., Landry, J.: 2018 Effective topology tampering attacks and defenses in software-defined networks. In: Proceeding of 2018 48th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks, pp. 374-386,","DOI":"10.1109\/DSN.2018.00047"},{"issue":"3","key":"4069_CR103","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1109\/TIFS.2017.2765506","volume":"13","author":"S Deng","year":"2018","unstructured":"Deng, S., Gao, X., Lu, Z., Gao, X.: Packet injection attack and its defense in software-defined networks. IEEE Trans. Inf. Forensics Secur. 13(3), 695\u2013705 (2018)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"4069_CR104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSM.2022.3226646","volume":"19","author":"TV Phan","year":"2022","unstructured":"Phan, T.V., Bauschert, T.: Deepair: deep reinforcement learning for intrusion response in software-defined networks. IEEE Trans. Net. Service Manag. 19, 1\u201312 (2022)","journal-title":"IEEE Trans. Net. Service Manag."},{"key":"4069_CR105","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2022.3172304","volume":"10","author":"MA Razib","year":"2022","unstructured":"Razib, M.A., Javeed, D., Khan, M.T., Alkanhel, R., Muthanna, M.S.A.: Cyber threats detection in smart environments using sdn-enabled dnn-lstm hybrid framework. IEEE Access 10, 1\u201312 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR106","first-page":"434","volume":"211","author":"Z Tu","year":"2020","unstructured":"Tu, Z., Zhou, H., Li, K., Li, M., Tian, A.: An energy-efficient topology design and ddos attacks mitigation for green software-defined satellite network. IEEE Access 211, 434\u2013451 (2020)","journal-title":"IEEE Access"},{"issue":"14","key":"4069_CR107","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3390\/s21144884","volume":"21","author":"D Javeed","year":"2021","unstructured":"Javeed, D., Gao, T., Khan, M.T., Ahmad, I.: A hybrid deep learning-driven sdn enabled mechanism for secure communication in internet of things (iot). Sensors 21(14), 48\u201384 (2021)","journal-title":"Sensors"},{"issue":"3","key":"4069_CR108","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/TMM.2019.2893549","volume":"21","author":"S Garg","year":"2019","unstructured":"Garg, S., Kaur, K., Kumar, N., Rodrigues, J.J.: Hybrid deep-learning-based anomaly detection scheme for suspicious flow detection in sdn: a social multimedia perspective. IEEE Trans. Multimedia 21(3), 566\u2013578 (2019)","journal-title":"IEEE Trans. Multimedia"},{"key":"4069_CR109","doi-asserted-by":"crossref","unstructured":"Hu, D., Hong, P., Chen, Y.: 2017 Fadm: Ddos flooding attack detection and mitigation system in software-defined networking. GLOBECOM 2017-2017 IEEE Global Communications Conference. IEEE, pp. 1-7, (2017)","DOI":"10.1109\/GLOCOM.2017.8254023"},{"issue":"5","key":"4069_CR110","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/dac.3497","volume":"31","author":"C Li","year":"2018","unstructured":"Li, C., Wu, Y., Yuan, X., Sun, Z., Wang, W., Li, X., Gong, L.: Detection and defense of ddos attack-based on deep learning in openflow-based sdn. Int. J. Commun. Syst. 31(5), 1\u201320 (2018)","journal-title":"Int. J. Commun. Syst."},{"key":"4069_CR111","first-page":"713","volume":"73","author":"Q Shafi","year":"2018","unstructured":"Shafi, Q., Basit, A., Qaisar, S., Koay, A., Welch, I.: Fog-assisted sdn controlled framework for enduring anomaly detection in an iot network. IEEE Access 73, 713\u2013724 (2018)","journal-title":"IEEE Access"},{"key":"4069_CR112","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1109\/ACCESS.2020.2999668","volume":"8","author":"M Yue","year":"2020","unstructured":"Yue, M., Wang, H., Liu, L., Wu, Z.: Detecting dos attacks based on multi-features in sdn. IEEE Access 8, 104\u2013688 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR113","unstructured":"Ali, A., Yousaf, M.\u00a0M.: Deep learning based intrusion detection system : software defined network. Asian Conference on Innovation in Technology (ASIANCON), (2021)"},{"key":"4069_CR114","doi-asserted-by":"crossref","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Dev, S., Jurcut, A.D., Ddosnet: A deep-learning model for detecting network attacks, in,: IEEE 21st International Symposium on A World of Wireless, Mobile and Multimedia Networks\"(WoWMoM). IEEE 2020, 391-396 (2020)","DOI":"10.1109\/WoWMoM49955.2020.00072"},{"key":"4069_CR115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TCCN.2021.3108813","volume":"8","author":"MS ElSayed","year":"2022","unstructured":"ElSayed, M.S., Le-Khac, N.-A., Azer, M.A., Jurcut, A.D.: A flow based anomaly detection approach with feature selection method against ddos attacks in sdns. IEEE Trans. Cognitive Commun. 8, 1\u201320 (2022)","journal-title":"IEEE Trans. Cognitive Commun."},{"key":"4069_CR116","first-page":"172","volume":"100","author":"GF Scaranti","year":"2020","unstructured":"Scaranti, G.F., Carvalho, L.F., Proenca, M.L.: Artificial immune systems and fuzzy logic to detect flooding attacks in software-defined networks. IEEE Access 100, 172\u2013185 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR117","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2021.103108","volume":"187","author":"N Ahuja","year":"2021","unstructured":"Ahuja, N., Singal, G., Mukhopadhyay, D., Kumar, N.: Automated ddos attack detection in software defined networking. J. Netw. Comput. Appl. 187, 1\u201320 (2021)","journal-title":"J. Netw. Comput. Appl."},{"key":"4069_CR118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2021.06.009","volume":"125","author":"MP Novaes","year":"2021","unstructured":"Novaes, M.P., Carvalho, L.F., Lloret, J., Jr., M. L. P.: Adversarial deep learning approach detection and defense against ddos attacks in sdn environments. Fut. Gene. Comput. Syst. 125, 1\u201320 (2021)","journal-title":"Fut. Gene. Comput. Syst."},{"key":"4069_CR119","first-page":"809","volume":"27","author":"H Peng","year":"2018","unstructured":"Peng, H., Sun, Z., Zhao, X., Tan, S., Sun, Z.: A detection method for anomaly flow in software defined network. IEEE Access 27, 809\u2013818 (2018)","journal-title":"IEEE Access"},{"key":"4069_CR120","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1109\/JIOT.2017.2694702","volume":"4","author":"D He","year":"2017","unstructured":"He, D., Chan, S., Ni, X., Guizani, M.: Software-defined-networking-enabled traffic anomaly detection and mitigation. IEEE Int. Things J. 4, 1890\u20131899 (2017)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR121","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TPDS.2021.3068135","volume":"32","author":"Q Li","year":"2021","unstructured":"Li, Q., Liu, Y., Liu, Z., Pang, C.: Efficient forwarding anomaly detection in software-defined networks. IEEE Transacctions on Parallel and Distributed Systems. 32, 2676\u20131697 (2021)","journal-title":"IEEE Transacctions on Parallel and Distributed Systems."},{"key":"4069_CR122","first-page":"8","volume":"15","author":"M Dhawan","year":"2015","unstructured":"Dhawan, M., Poddar, R., Mahajan, K., Mann, V.: Sphinx: detecting security attacks in software-defined networks. Ndss 15, 8\u201311 (2015)","journal-title":"Ndss"},{"issue":"21","key":"4069_CR123","first-page":"1","volume":"30","author":"F Musumeci","year":"2022","unstructured":"Musumeci, F., Fidanci, A.C., Paolucci, F., Cugini, F., Tornatore, M.: Machine-learning-enabled ddos attacks detection in p4 programmable networks. J. Net. Syst. Manag. vol. 30(21), 1\u201327 (2022)","journal-title":"J. Net. Syst. Manag. vol."},{"issue":"4","key":"4069_CR124","doi-asserted-by":"publisher","first-page":"4353","DOI":"10.1109\/TNSM.2021.3094514","volume":"18","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Cui, L., Tso, F.P., Jia, W.: pheavy: predicting heavy flows in the programmable data plane. IEEE Trans. Netw. Serv. Manage. 18(4), 4353\u20134365 (2021)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"issue":"3","key":"4069_CR125","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1109\/TNSM.2020.3048265","volume":"18","author":"A da Silveira Ilha","year":"2021","unstructured":"da Silveira Ilha, A., Cardoso Lapolli, \u00c2., Marques, J.A., Gaspary, L.P.: Euclid: a fully in-network, p4-based approach for real-time ddos attack detection and mitigation. IEEE Trans. Net. Serv. Manag. 18(3), 3121\u20133140 (2021)","journal-title":"IEEE Trans. Net. Serv. Manag."},{"key":"4069_CR126","unstructured":"The caida ucsd anonymized internet traces 2016. [Online]. Available: https:\/\/www.caida.org\/data\/passive\/passive_2016_dataset.xml"},{"key":"4069_CR127","unstructured":"The caida ucsd ddos attack 2007 dataset. [Online]. Available: ttp:\/\/www.caida.org\/data\/passive\/ddos-20070804_dataset.xml"},{"key":"4069_CR128","doi-asserted-by":"crossref","unstructured":"Shin, S., Gu, G.: Attacking software-defined networks: A first feasibility study. In: Proc. Second ACM SIGCOMM Work. Hot Top. Softw. Defin. Netw., pp. 165-166, (2013)","DOI":"10.1145\/2491185.2491220"},{"key":"4069_CR129","doi-asserted-by":"crossref","unstructured":"Kl\u00f6ti, R., Kotronis, V., Smith, P.: Openflow: a security analysis. In Proceedings of International Conference on Network Protocols (ICNP), pp. 1-6, (2013)","DOI":"10.1109\/ICNP.2013.6733671"},{"key":"4069_CR130","doi-asserted-by":"crossref","unstructured":"Zhang, M., Hou, J., Zhang, Z., Shi, W., Qin, B., Liang, B., Fine-grained fingerprinting threats to software-defined networks, in,: IEEE Trustcom\/BigDataSE\/ICESS. IEEE 2017, 128\u2013135 (2017)","DOI":"10.1109\/Trustcom\/BigDataSE\/ICESS.2017.229"},{"key":"4069_CR131","doi-asserted-by":"crossref","unstructured":"Sonchack, J., Aviv, A.\u00a0J., Keller, E.: Timing sdn control planes to infer network configurations In Proceedings of the 2016 ACM International Workshop on Security in Software Defined Networks & Network Function Virtualization, pp. 19\u201322 , (2016)","DOI":"10.1145\/2876019.2876030"},{"issue":"4","key":"4069_CR132","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/MNET.2018.1700283","volume":"32","author":"BXS Gao","year":"2018","unstructured":"Gao, B.X.S., Li, Z., Wei, G.: Security threats in the data plane of software-defined networks. IEEE Netw. 32(4), 108\u2013113 (2018)","journal-title":"IEEE Netw."},{"issue":"1","key":"4069_CR133","first-page":"467","volume":"18","author":"F Farhin","year":"2021","unstructured":"Farhin, F., Sultana, I., Islam, N., Kaiser, M.S., Rahman, M.S., Mahmud, M.: Attack detection in internet of things using software defined network and fuzzy neural network. IEEE Trans. Industr. Inf. 18(1), 467\u2013476 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"4069_CR134","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.comcom.2019.09.014","volume":"148","author":"P Krishnan","year":"2019","unstructured":"Krishnan, P., Duttagupta, S., Achuthan, K.: Varman: multi-plane security framework for software defined networks. Comput. Commun. 148, 215\u2013239 (2019)","journal-title":"Comput. Commun."},{"key":"4069_CR135","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107757","volume":"99","author":"N Ahuja","year":"2022","unstructured":"Ahuja, N., Singal, G., Mukhopadhyay, D., Nehra, A.: Ascertain the efficient machine learning approach to detect different arp attacks. Comput. Elect. Eng. 99, 107757 (2022)","journal-title":"Comput. Elect. Eng."},{"key":"4069_CR136","doi-asserted-by":"crossref","unstructured":"Lee, C., Yoon, C., Shin, S., Cha, S.: Indago: a new framework for detecting malicious sdn applications. In: Proceedings of 2018 IEEE 26th International Conference on Network Protocols (ICNP), pp. 220-230, (2018)","DOI":"10.1109\/ICNP.2018.00031"},{"key":"4069_CR137","unstructured":"Cao, J., Li, Q., Xie, R., Sun, K., Gu, G., Xu, M., Yang, Y.: The crosspath attack: disrupting the sdn control channel via shared links. In: Proceedings of 28th USENIX Security Symposium, pp. 1-18, (2019)"},{"key":"4069_CR138","doi-asserted-by":"crossref","unstructured":"Khamaiseh, S., Serra, E., Li, Z., Xu, D.: Detecting saturation attacks in sdn via machine learning. 4th International Conference on Computing, Communications and Security (ICCCS), (2019)","DOI":"10.1109\/CCCS.2019.8888049"},{"key":"4069_CR139","doi-asserted-by":"crossref","unstructured":"Divekar, M.\u00a0P., Savla, V., Mishra, R., Shirole, M.: Benchmarking datasets for anomaly-based network intrusion detection: Kdd cup 99 alternatives. Proc. IEEE 3rd Int. Conf. Comput., Commun. Secur. (ICCCS), pp. 1-8, (2018)","DOI":"10.1109\/CCCS.2018.8586840"},{"key":"4069_CR140","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.\u00a0A.: A detailed analysis of the kdd cup 99 data set. In Proc. IEEE Symp. Comput. Intell. Secur. Defense Appl., pp. 1-6, (2009)","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"4069_CR141","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1016\/j.cose.2011.12.012","volume":"31","author":"H Shiravi","year":"2012","unstructured":"Shiravi, H., Shiravi, M.T., Ghorbani, A.A.: Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput. Security 31, 357\u2013374 (2012)","journal-title":"Comput. Security"},{"key":"4069_CR142","first-page":"18","volume":"25","author":"N Moustaf","year":"2016","unstructured":"Moustaf, N., Slay, J.: The evaluation of network anomaly detection systems: statistical analysis of the unsw-nb15 data set and the comparison with the kdd99 data set. Inform. Security J. 25, 18\u201331 (2016)","journal-title":"Inform. Security J."},{"key":"4069_CR143","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/COMST.2015.2402161","volume":"18","author":"C Kolias","year":"2016","unstructured":"Kolias, C., Kambourakis, G., Stavrou, A., Gritzalis, S.: Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun. Surveys Tuts. 18, 184\u2013208 (2016)","journal-title":"IEEE Commun. Surveys Tuts."},{"key":"4069_CR144","doi-asserted-by":"crossref","first-page":"108","DOI":"10.21125\/iceri.2018.1026","volume":"1","author":"A Sharafaldin","year":"2018","unstructured":"Sharafaldin, A., Lashkari, H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. Proc. ICISSP. 1, 108\u2013116 (2018)","journal-title":"Proc. ICISSP."},{"key":"4069_CR145","unstructured":"of\u00a0Cybersecurity, C.\u00a0I.: Cse-cic-ids2018. Accessed July 10, 2022, [Online]"},{"key":"4069_CR146","unstructured":"Ring, M., Wunderlich, S., Gr\u00fcdl, D., Landes, D., Hotho, A.,: \u201cFlow-based benchmark data sets for intrusion detection. In: Eur. Conf. Inf. Warf. Secur. ECCWS, pp. 361-369, 2017"},{"key":"4069_CR147","doi-asserted-by":"crossref","unstructured":"Sharafaldin, A.\u00a0H., Lashkari, S.\u00a0H., Ghorbani, A.\u00a0A.: Developing realistic distributed denial of service (ddos) attack dataset and taxonomy. In Proc. Int. Carnahan Conf. Secur. Technol. (ICCST), pp. 1\u20138, (2019)","DOI":"10.1109\/CCST.2019.8888419"},{"key":"4069_CR148","unstructured":"Song, H.\u00a0T., Okabe, Y.: Description of kyoto university benchmark data, (2006)"},{"key":"4069_CR149","doi-asserted-by":"crossref","unstructured":"ElSayed, M.\u00a0S., Le-Khac, N.-A., Jorcot, A.\u00a0D. : Insdn: a novel sdn intrusion dataset. IEEE Access, pp. 165-623, (2020)","DOI":"10.1109\/ACCESS.2020.3022633"},{"key":"4069_CR150","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.1109\/TITS.2020.2988065","volume":"22","author":"S Garg","year":"2021","unstructured":"Garg, S., Singh, A., Aujla, G.S., Kaur, S., Batra, S., Kumar, N.: Probabilistic data structures-based anomaly detection scheme for software-defined internet of vehicles. IEEE Trans. Intell. Transport. Syst. 22, 3557\u20133567 (2021)","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"4069_CR151","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1109\/JIOT.2020.3011521","volume":"8","author":"B Wang","year":"2021","unstructured":"Wang, B., Sun, Y., Xu, X.: A scalable and energy-efficient anomaly detection scheme in wireless sdn-based mmtc networks for iot. IEEE Int. Things J. 8, 1388\u20131406 (2021)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR152","first-page":"606","volume":"24","author":"D Yin","year":"2018","unstructured":"Yin, D., Zhang, L., Yang, K.: A ddos attack detection and mitigation with software-defined internet of things framework. IEEE Access 24, 606\u2013624 (2018)","journal-title":"IEEE Access"},{"key":"4069_CR153","doi-asserted-by":"publisher","first-page":"9485","DOI":"10.1109\/ACCESS.2017.2702341","volume":"5","author":"MVOD Assis","year":"2017","unstructured":"Assis, M.V.O.D., Hamamoto, A.H., Abrao, T., Proenca, M.L.: A game theoretical based system using holt-winters and genetic algorithm with fuzzy logic for dos\/ddos mitigation on sdn networks. IEEE Access 5, 9485\u20139497 (2017)","journal-title":"IEEE Access"},{"issue":"3","key":"4069_CR154","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1109\/TNSM.2020.2997734","volume":"17","author":"N Ravi","year":"2020","unstructured":"Ravi, N., Shalinie, S.M., Theres, D.D.J.: Balance: Link flooding attack detection and mitigation via hybrid-sdn. IEEE Trans. Netw. Serv. Manage. 17(3), 1715\u20131730 (2020)","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"4069_CR155","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1109\/TNSM.2018.2861741","volume":"15","author":"P Kumar","year":"2018","unstructured":"Kumar, P., Tripathi, M., Nehra, A., Conti, M., Lal, C.: Safety: early detection and mitigation of tcp syn flood utilizing entropy in sdn. IEEE Trans. Net. Service Manag. 15, 1545\u20131560 (2018)","journal-title":"IEEE Trans. Net. Service Manag."},{"key":"4069_CR156","first-page":"593","volume":"102","author":"I Aliyu","year":"2021","unstructured":"Aliyu, I., Feliciano, M.C., Engelenburg, S.V., Kim, D.O., Lim, C.G.: A blockchain-based federated forest for sdn-enabled in-vehicle network intrusion detection system. IEEE Access 102, 593\u2013619 (2021)","journal-title":"IEEE Access"},{"key":"4069_CR157","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1109\/JIOT.2018.2883344","volume":"6","author":"J Li","year":"2019","unstructured":"Li, J., Zhao, Z., Li, R., Zhang, H.: Ai-based two-stage intrusion detection for software defined iot networks. IEEE Int. Things J. 6, 2093\u20132103 (2019)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR158","doi-asserted-by":"publisher","first-page":"7746","DOI":"10.1109\/JIOT.2021.3114270","volume":"9","author":"GAN Segura","year":"2022","unstructured":"Segura, G.A.N., Chorti, A., Margi, C.B.: Centralized and distributed intrusion detection for resource-constrained wireless sdn networks. IEEE Int. Things J. 9, 7746\u20137759 (2022)","journal-title":"IEEE Int. Things J."},{"key":"4069_CR159","first-page":"481","volume":"66","author":"AH Janabi","year":"2022","unstructured":"Janabi, A.H., Kanakis, T., Johnson, M.: Overhead reduction technique for software-defined network based intrusion detection systems. IEEE Access 66, 481\u2013492 (2022)","journal-title":"IEEE Access"},{"key":"4069_CR160","first-page":"066","volume":"114","author":"M Bagaa","year":"2020","unstructured":"Bagaa, M., Taleb, T., Bernabe, J.B., Skarmeta, A.: A machine learning security framework for iot systems. IEEE Access 114, 066\u2013078 (2020)","journal-title":"IEEE Access"},{"key":"4069_CR161","doi-asserted-by":"publisher","first-page":"5730","DOI":"10.1109\/TII.2020.3012166","volume":"17","author":"G Raja","year":"2021","unstructured":"Raja, G., Anbalagan, S., Vijayaraghavan, G., Dhanasekaran, P., Al-Otaibi, Y.D., Bashir, A.K.: Energy-efficient end-to-end security for software-defined vehicular networks. IEEE Trans. Industrial Informatics 17, 5730\u20135738 (2021)","journal-title":"IEEE Trans. Industrial Informatics"},{"key":"4069_CR162","unstructured":"Assis, M.\u00a0V. O.\u00a0D., Novaes, M.\u00a0P., . Zerbini, C.\u00a0B, Carvalho, L.\u00a0F., Abrao, T., Jr, M.\u00a0L.\u00a0P.: \u201cFast defense system against attacks in software defined networks,\u201d IEEE Access, pp. pp. 69\u00a0620\u201369\u00a0640, 2018"},{"key":"4069_CR163","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Cheng, G., Yu, S.: \u201cAn sdn-enabled proactive defense framework for ddos mitigation in iot networks,\u201d IEEE Transactions on Information Forensics and Security, pp. pp. 5366\u20135381, 2021","DOI":"10.1109\/TIFS.2021.3127009"},{"key":"4069_CR164","doi-asserted-by":"crossref","unstructured":"Vishwakarma, L., Nahar, A., Das, D.: \u201cLbsv: Lightweight blockchain security protocol for secure storage and communication in sdn-enabled iov,\u201d IEEE Transactions on Vehicular Technology, pp. pp. 5983\u20135995, 2022","DOI":"10.1109\/TVT.2022.3163960"},{"key":"4069_CR165","doi-asserted-by":"crossref","unstructured":"L.\u00a0F.\u00a0M. et\u00a0al.,: \u201cA self-adaptive deep learning-based system for anomaly detection in 5g networks,\u201d IEEE Access, vol. 6, pp. pp. 7700\u20137712, 2018","DOI":"10.1109\/ACCESS.2018.2803446"},{"key":"4069_CR166","doi-asserted-by":"crossref","unstructured":"Sahoo, D., Pham, Q., Lu, J., Hoi, S.\u00a0C.: \u201cOnline deep learning: Learning deep neural networks on the fly,\u201d arXiv preprint arXiv:1711.03705, 2017","DOI":"10.24963\/ijcai.2018\/369"},{"key":"4069_CR167","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., ang, C.\u00a0Y, Liu, C.: \u201cA survey on deep transfer learning,\u201d in International conference on artificial neural networks. Springer, 2018, pp. 270\u2013279","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"4069_CR168","doi-asserted-by":"crossref","unstructured":"Alonso, R.\u00a0S., Sitt\u00f3n-Candanedo, I., Casado-Vara, R., Prieto, J., Corchado, J.\u00a0M.: \u201cDeep reinforcement learning for the management of software-defined networks in smart farming,\u201d in 2020 International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2020, pp. 1\u20136","DOI":"10.1109\/COINS49042.2020.9191634"},{"key":"4069_CR169","doi-asserted-by":"crossref","unstructured":"Phan, T.\u00a0V., Sultana, S., Nguyen, T.\u00a0G., Bauschert, T.: \u201c$$q$$-transfer: A novel framework for efficient deep transfer learning in networking,\u201d in 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020, pp. 146\u2013151","DOI":"10.1109\/ICAIIC48513.2020.9065240"},{"key":"4069_CR170","doi-asserted-by":"crossref","unstructured":"R.\u00a0S. et\u00a0al.:, \u201cMdp and machine learning-based cost-optimization of dynamic resource allocation for network function virtualization,\u201d In: Proceedings of IEEE International Conference on Service Computing, pp. pp. 65\u201373, 2015","DOI":"10.1109\/SCC.2015.19"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-04069-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-023-04069-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-023-04069-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T06:41:33Z","timestamp":1729752093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-023-04069-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,15]]},"references-count":170,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["4069"],"URL":"https:\/\/doi.org\/10.1007\/s10586-023-04069-9","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,15]]},"assertion":[{"value":"19 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2023","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 have not disclosed any competing interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}