{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T03:24:00Z","timestamp":1783481040418,"version":"3.55.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T00:00:00Z","timestamp":1750032000000},"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":[[2025,9]]},"DOI":"10.1007\/s10586-024-05003-3","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T12:11:29Z","timestamp":1750075889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel CNN-enhanced detection and mitigation of DDoS attacks in SDN"],"prefix":"10.1007","volume":"28","author":[{"given":"Ashfaq Ahmad","family":"Najar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. Manohar","family":"Naik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal Rasheed","family":"Lone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azra","family":"Nazir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"5003_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103716","volume":"139","author":"AA Najar","year":"2024","unstructured":"Najar, A.A., Manohar Naik, S.: Cyber-secure SDN: a CNN-based approach for efficient detection and mitigation of DDoS attacks. Comput. Secur. 139, 103716 (2024). https:\/\/doi.org\/10.1016\/j.cose.2024.103716","journal-title":"Comput. Secur."},{"key":"5003_CR2","doi-asserted-by":"publisher","unstructured":"Steinberger, J., Kuhnert, B., Dietz, C., Ball, L., Sperotto, A., Baier, H., Pras, A., Dreo, G.: DDoS defense using MTD and SDN. In: NOMS 2018\u20142018 IEEE\/IFIP Network Operations and Management Symposium, 2018, pp. 1\u20139. https:\/\/doi.org\/10.1109\/NOMS.2018.8406221","DOI":"10.1109\/NOMS.2018.8406221"},{"issue":"3","key":"5003_CR3","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s10922-023-09741-4","volume":"31","author":"T Linhares","year":"2023","unstructured":"Linhares, T., Patel, A., Barros, A.L., Fernandez, M.: SDNTruth: innovative DDoS detection scheme for software-defined networks (SDN). J. Netw. Syst. Manage. 31(3), 55 (2023). https:\/\/doi.org\/10.1007\/s10922-023-09741-4","journal-title":"J. Netw. Syst. Manage."},{"key":"5003_CR4","doi-asserted-by":"publisher","unstructured":"Najar, A.A., M.N. S.: A robust DDoS intrusion detection system using convolutional neural network. Comput. Electr. Eng. 117, 109277 (2024). https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109277","DOI":"10.1016\/j.compeleceng.2024.109277"},{"issue":"19","key":"5003_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.8157","volume":"36","author":"AA Najar","year":"2024","unstructured":"Najar, A.A., Sugali, M.N., Lone, F.R., Nazir, A.: A novel CNN-based approach for detection and classification of DDoS attacks. Concurr. Comput. Pract. Exp. 36(19), e8157 (2024). https:\/\/doi.org\/10.1002\/cpe.8157","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"5003_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103661","volume":"138","author":"V Hnamte","year":"2024","unstructured":"Hnamte, V., Najar, A.A., Nhung-Nguyen, H., Hussain, J., Sugali, M.N.: DDoS attack detection and mitigation using deep neural network in SDN environment. Comput. Secur. 138, 103661 (2024). https:\/\/doi.org\/10.1016\/j.cose.2023.103661","journal-title":"Comput. Secur."},{"key":"5003_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100279","volume":"37","author":"J Singh","year":"2020","unstructured":"Singh, J., Behal, S.: Detection and mitigation of DDoS attacks in SDN: a comprehensive review, research challenges and future directions. Comput. Sci. Rev. 37, 100279 (2020). https:\/\/doi.org\/10.1016\/j.cosrev.2020.100279","journal-title":"Comput. Sci. Rev."},{"key":"5003_CR8","doi-asserted-by":"publisher","first-page":"80813","DOI":"10.1109\/ACCESS.2019.2922196","volume":"7","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 7, 80813\u201380828 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2922196","journal-title":"IEEE Access"},{"key":"5003_CR9","doi-asserted-by":"publisher","unstructured":"Singh, A., Kaur, H., Kaur, N.: A novel DDoS detection and mitigation technique using hybrid machine learning model and redirect illegitimate traffic in SDN network. Clust. Comput. (2023). https:\/\/doi.org\/10.1007\/s10586-023-04152-1","DOI":"10.1007\/s10586-023-04152-1"},{"issue":"3","key":"5003_CR10","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","volume":"31","author":"C Janiesch","year":"2021","unstructured":"Janiesch, C., Zschech, P., Heinrich, K.: Machine learning and deep learning. Electron. Mark. 31(3), 685\u2013695 (2021). https:\/\/doi.org\/10.1007\/s12525-021-00475-2","journal-title":"Electron. Mark."},{"key":"5003_CR11","doi-asserted-by":"publisher","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, 103108 (2021). https:\/\/doi.org\/10.1016\/j.jnca.2021.103108","journal-title":"J. Netw. Comput. Appl."},{"key":"5003_CR12","doi-asserted-by":"publisher","unstructured":"Mukkamala, S., Sung, A., Bandyopadhyay, S., Maulik, U., Holder, L., Cook, D.: Significant feature selection using computational intelligent techniques for intrusion detection. In: Advanced Methods for Knowledge Discovery from Complex Data, pp. 285\u2013306 (2005). https:\/\/doi.org\/10.1007\/1-84628-284-5_11","DOI":"10.1007\/1-84628-284-5_11"},{"issue":"5","key":"5003_CR13","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1080\/08839514.2019.1582861","volume":"33","author":"P Sornsuwit","year":"2019","unstructured":"Sornsuwit, P., Jaiyen, S.: A new hybrid machine learning for cybersecurity threat detection based on adaptive boosting. Appl. Artif. Intell. 33(5), 462\u2013482 (2019). https:\/\/doi.org\/10.1080\/08839514.2019.1582861","journal-title":"Appl. Artif. Intell."},{"issue":"5","key":"5003_CR14","doi-asserted-by":"publisher","first-page":"2623","DOI":"10.1007\/s41870-023-01332-5","volume":"15","author":"V Hnamte","year":"2023","unstructured":"Hnamte, V., Hussain, J.: An efficient DDoS attack detection mechanism in SDN environment. Int. J. Inf. Technol. 15(5), 2623\u20132636 (2023). https:\/\/doi.org\/10.1007\/s41870-023-01332-5","journal-title":"Int. J. Inf. Technol."},{"issue":"19","key":"5003_CR15","doi-asserted-by":"publisher","first-page":"10743","DOI":"10.3390\/su131910743","volume":"13","author":"MJ Awan","year":"2021","unstructured":"Awan, M.J., Farooq, U., Babar, H.M.A., Yasin, A., Nobanee, H., Hussain, M., Hakeem, O., Zain, A.M.: Real-time DDoS attack detection system using big data approach. Sustainability 13(19), 10743 (2021). https:\/\/doi.org\/10.3390\/su131910743","journal-title":"Sustainability"},{"key":"5003_CR16","unstructured":"Cloudflare: DDoS threat report for 2024 third quater (2024). https:\/\/blog.cloudflare.com\/ddos-threat-report-for-2024-q3\/"},{"key":"5003_CR17","unstructured":"Paganini, P.: New \u201chttp\/2 rapid reset\u201d technique behind record-breaking DDoS attacks. https:\/\/securityaffairs.com\/152278\/hacking\/ddos-http-2-rapid-reset-technique.html (2023)"},{"key":"5003_CR18","unstructured":"Goodin, D., Goodin, D.: Microsoft fends off record-breaking 3.47 tbps DDoS attack. Ars Technica. https:\/\/arstechnica.com\/information-technology\/2022\/01\/microsoft-fends-off-record-breaking-3-47-tbps-ddos-attack\/(2022)"},{"key":"5003_CR19","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. Comput. Commun. 154, 509\u2013527 (2020). https:\/\/doi.org\/10.1016\/j.comcom.2020.02.085","journal-title":"Comput. Commun."},{"key":"5003_CR20","doi-asserted-by":"publisher","first-page":"5039","DOI":"10.1109\/ACCESS.2019.2963077","volume":"8","author":"S Dong","year":"2020","unstructured":"Dong, S., Sarem, M.: DDoS attack detection method based on improved KNN with the degree of DDoS attack in software-defined networks. IEEE Access 8, 5039\u20135048 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2019.2963077","journal-title":"IEEE Access"},{"issue":"4","key":"5003_CR21","doi-asserted-by":"publisher","first-page":"3042","DOI":"10.1109\/TNSE.2020.3012002","volume":"7","author":"L Tan","year":"2020","unstructured":"Tan, L., Huang, K., Peng, G., Chen, G.: Stability of TCP\/AQM networks under DDoS attacks with design. IEEE Trans. Netw. Sci. Eng. 7(4), 3042\u20133056 (2020). https:\/\/doi.org\/10.1109\/TNSE.2020.3012002","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"13","key":"5003_CR22","doi-asserted-by":"publisher","first-page":"11604","DOI":"10.1109\/JIOT.2021.3130156","volume":"9","author":"IA Khan","year":"2022","unstructured":"Khan, I.A., Moustafa, N., Pi, D., Sallam, K.M., Zomaya, A.Y., Li, B.: A new explainable deep learning framework for cyber threat discovery in industrial IoT networks. IEEE Internet Things J. 9(13), 11604\u201311613 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3130156","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"5003_CR23","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. Manag. 31(3), 54 (2023). https:\/\/doi.org\/10.1007\/s10922-023-09749-w","journal-title":"J. Netw. Syst. Manag."},{"key":"5003_CR24","doi-asserted-by":"publisher","unstructured":"Chaganti, R., Suliman, W., Ravi, V., Dua, A.: Deep learning approach for SDN-enabled intrusion detection system in IoT networks. Information 14(1) (2023). https:\/\/doi.org\/10.3390\/info14010041","DOI":"10.3390\/info14010041"},{"key":"5003_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2023.109935","volume":"234","author":"AT Phu","year":"2023","unstructured":"Phu, A.T., Li, B., Ullah, F., Huque, T.U., Naha, R., Babar, M.A., Nguyen, H.: Defending SDN against packet injection attacks using deep learning. Comput. Netw. 234, 109935 (2023). https:\/\/doi.org\/10.1016\/j.comnet.2023.109935","journal-title":"Comput. Netw."},{"issue":"5","key":"5003_CR26","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1007\/s41870-022-01003-x","volume":"14","author":"AA Najar","year":"2022","unstructured":"Najar, A.A., Manohar Naik, S.: DDoS attack detection using MLP and Random Forest algorithms. Int. J. Inf. Technol. 14(5), 2317\u20132327 (2022). https:\/\/doi.org\/10.1007\/s41870-022-01003-x","journal-title":"Int. J. Inf. Technol."},{"key":"5003_CR27","doi-asserted-by":"publisher","unstructured":"Najar, A.A., Naik, S.M.: Applying supervised machine learning techniques to detect DDoS attacks. In: 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), pp. 1\u20137 (2022). https:\/\/doi.org\/10.1109\/ASIANCON55314.2022.9908654","DOI":"10.1109\/ASIANCON55314.2022.9908654"},{"key":"5003_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2024.103845","volume":"90","author":"MA Setitra","year":"2024","unstructured":"Setitra, M.A., Fan, M.: Detection of DDoS attacks in SDN-based VANET using optimized TabNet. Comput. Stand. Interfaces 90, 103845 (2024). https:\/\/doi.org\/10.1016\/j.csi.2024.103845","journal-title":"Comput. Stand. Interfaces"},{"key":"5003_CR29","doi-asserted-by":"publisher","unstructured":"Bishla, D., Kumar, B.: Performance enhancement in hybrid SDN using advanced deep learning with multi-objective optimization frameworks under heterogeneous environments. Int. J. Commun. Syst. e5989 (2024). https:\/\/doi.org\/10.1002\/dac.5989","DOI":"10.1002\/dac.5989"},{"key":"5003_CR30","doi-asserted-by":"publisher","unstructured":"Swathi, B., Kolisetty, S.S., Sivanarayana, G.V., Battula, S.R.: Efficientnetv2-regnet: an effective deep learning framework for secure SDN based IoT network. Clust. Comput. 1\u201318 (2024). https:\/\/doi.org\/10.1007\/s10586-024-04498-0","DOI":"10.1007\/s10586-024-04498-0"},{"issue":"2","key":"5003_CR31","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s10207-023-00771-2","volume":"23","author":"M Maddu","year":"2024","unstructured":"Maddu, M., Rao, Y.N.: Network intrusion detection and mitigation in SDN using deep learning models. Int. J. Inf. Secur. 23(2), 849\u2013862 (2024). https:\/\/doi.org\/10.1007\/s10207-023-00771-2","journal-title":"Int. J. Inf. Secur."},{"issue":"1","key":"5003_CR32","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/s42400-024-00219-7","volume":"7","author":"UB Clinton","year":"2024","unstructured":"Clinton, U.B., Hoque, N., Robindro Singh, K.: Classification of DDoS attack traffic on SDN network environment using deep learning. Cybersecurity 7(1), 23 (2024). https:\/\/doi.org\/10.1186\/s42400-024-00219-7","journal-title":"Cybersecurity"},{"issue":"2","key":"5003_CR33","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s10207-023-00785-w","volume":"23","author":"DS Rao","year":"2024","unstructured":"Rao, D.S., Emerson, A.J.: Cyberattack defense mechanism using deep learning techniques in software-defined networks. Int. J. Inf. Secur. 23(2), 1279\u20131291 (2024). https:\/\/doi.org\/10.1007\/s10207-023-00785-w","journal-title":"Int. J. Inf. Secur."},{"issue":"1","key":"5003_CR34","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1804\/1\/012136","volume":"1804","author":"SA Abbas","year":"2021","unstructured":"Abbas, S.A., Almhanna, M.S.: Distributed denial of service attacks detection system by machine learning based on dimensionality reduction. J. Phys. Conf. Ser. 1804(1), 012136 (2021). https:\/\/doi.org\/10.1088\/1742-6596\/1804\/1\/012136","journal-title":"J. Phys. Conf. Ser."},{"key":"5003_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2021.114765","volume":"174","author":"EO Omuya","year":"2021","unstructured":"Omuya, E.O., Okeyo, G.O., Kimwele, M.W.: Feature selection for classification using principal component analysis and information gain. Expert Syst. Appl. 174, 114765 (2021). https:\/\/doi.org\/10.1016\/J.ESWA.2021.114765","journal-title":"Expert Syst. Appl."},{"key":"5003_CR36","doi-asserted-by":"publisher","unstructured":"Kurniabudi, K., Harris, A., Veronica, V., Yanti, E.: Optimizing attack detection for high dimensionality and imbalanced data with smote, chi-square and random forest classifier. IJICS (Int. J. Inform. Comput. Sci.) (2022). https:\/\/doi.org\/10.30865\/ijics.v6i1.3890","DOI":"10.30865\/ijics.v6i1.3890"},{"issue":"3","key":"5003_CR37","doi-asserted-by":"publisher","first-page":"3533","DOI":"10.1007\/s13369-023-08075-2","volume":"49","author":"N Aslam","year":"2024","unstructured":"Aslam, N., Srivastava, S., Gore, M.: A comprehensive analysis of machine learning-and deep learning-based solutions for DDoS attack detection in SDN. Arab. J. Sci. Eng. 49(3), 3533\u20133573 (2024). https:\/\/doi.org\/10.1007\/s13369-023-08075-2","journal-title":"Arab. J. Sci. Eng."},{"key":"5003_CR38","doi-asserted-by":"publisher","first-page":"17982","DOI":"10.1109\/ACCESS.2024.3360868","volume":"12","author":"NS Musa","year":"2024","unstructured":"Musa, N.S., Mirza, N.M., Rafique, S.H., Abdallah, A.M., Murugan, T.: Machine learning and deep learning techniques for distributed denial of service anomaly detection in software defined networks-current research solutions. IEEE Access 12, 17982\u201318011 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3360868","journal-title":"IEEE Access"},{"key":"5003_CR39","doi-asserted-by":"publisher","first-page":"42120","DOI":"10.1109\/ACCESS.2020.2976706","volume":"8","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Yin, X., Hu, Y.: CPSS LR-DDoS detection and defense in edge computing utilizing DCNN Q-Learning. IEEE Access 8, 42120\u201342130 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2976706","journal-title":"IEEE Access"},{"key":"5003_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.107042","volume":"168","author":"W Elmasry","year":"2020","unstructured":"Elmasry, W., Akbulut, A., Zaim, A.H.: Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic. Comput. Netw. 168, 107042 (2020). https:\/\/doi.org\/10.1016\/j.comnet.2019.107042","journal-title":"Comput. Netw."},{"key":"5003_CR41","doi-asserted-by":"publisher","first-page":"155859","DOI":"10.1109\/ACCESS.2020.3019330","volume":"8","author":"JA P\u00e9rez-D\u00e1az","year":"2020","unstructured":"P\u00e9rez-D\u00e1az, J.A., Valdovinos, I.A., Choo, K.-K.R., Zhu, D.: A flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks using machine learning. IEEE Access 8, 155859\u2013155872 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3019330","journal-title":"IEEE Access"},{"key":"5003_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2019.102419","volume":"50","author":"MA Ferrag","year":"2020","unstructured":"Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020). https:\/\/doi.org\/10.1016\/j.jisa.2019.102419","journal-title":"J. Inf. Secur. Appl."},{"key":"5003_CR43","doi-asserted-by":"publisher","unstructured":"Said\u00a0Elsayed, M., Le-Khac, N.-A., Dev, S., Jurcut, A.D.: Network anomaly detection using LSTM based autoencoder. In: Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks, 2020, pp. 37\u201345 (2020). https:\/\/doi.org\/10.1145\/3416013.3426457","DOI":"10.1145\/3416013.3426457"},{"issue":"3","key":"5003_CR44","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.3390\/su13031522","volume":"13","author":"RMA Ujjan","year":"2021","unstructured":"Ujjan, R.M.A., Pervez, Z., Dahal, K., Khan, W.A., Khattak, A.M., Hayat, B.: Entropy based features distribution for anti-DDoS model in SDN. Sustainability 13(3), 1522 (2021). https:\/\/doi.org\/10.3390\/su13031522","journal-title":"Sustainability"},{"key":"5003_CR45","doi-asserted-by":"publisher","unstructured":"Khan, I.A., Pi, D., Khan, N., Khan, Z.U., Hussain, Y., Nawaz, A., Ali, F.: A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks. Appl. Intell. 1\u201316 (2021). https:\/\/doi.org\/10.1007\/s10489-021-02222-8","DOI":"10.1007\/s10489-021-02222-8"},{"key":"5003_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114520","volume":"169","author":"AE Cil","year":"2021","unstructured":"Cil, A.E., Yildiz, K., Buldu, A.: Detection of DDoS attacks with feed forward based deep neural network model. Expert Syst. Appl. 169, 114520 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114520","journal-title":"Expert Syst. Appl."},{"key":"5003_CR47","doi-asserted-by":"publisher","unstructured":"Hussain, J., Hnamte, V.: A novel deep learning based intrusion detection system: software defined network. In: 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021, pp. 506\u2013511 (2021). https:\/\/doi.org\/10.1109\/3ICT53449.2021.9581404","DOI":"10.1109\/3ICT53449.2021.9581404"},{"key":"5003_CR48","doi-asserted-by":"publisher","first-page":"146810","DOI":"10.1109\/ACCESS.2021.3123791","volume":"9","author":"Y Wei","year":"2021","unstructured":"Wei, Y., Jang-Jaccard, J., Sabrina, F., Singh, A., Xu, W., Camtepe, S.: Ae-mlp: a hybrid deep learning approach for DDoS detection and classification. IEEE Access 9, 146810\u2013146821 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3123791","journal-title":"IEEE Access"},{"key":"5003_CR49","doi-asserted-by":"publisher","unstructured":"Agarwal, A., Khari, M., Singh, R.: Detection of DDoS attack using deep learning model in cloud storage application. Wirel. Pers. Commun. 1\u201321 (2021). https:\/\/doi.org\/10.1007\/s11277-021-08271-z","DOI":"10.1007\/s11277-021-08271-z"},{"key":"5003_CR50","doi-asserted-by":"publisher","unstructured":"Zainudin, A., Ahakonye, L.A.C., Akter, R., Kim, D.-S., Lee, J.-M.: An efficient hybrid-DNN for DDoS detection and classification in software-defined IIoT networks. IEEE Internet Things J. 1\u20131 (2022). https:\/\/doi.org\/10.1109\/JIOT.2022.3196942","DOI":"10.1109\/JIOT.2022.3196942"},{"key":"5003_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2021.104412","volume":"89","author":"A Maheshwari","year":"2022","unstructured":"Maheshwari, A., Mehraj, B., Khan, M.S., Idrisi, M.S.: An optimized weighted voting based ensemble model for DDoS attack detection and mitigation in SDN environment. Microprocess. Microsyst. 89, 104412 (2022). https:\/\/doi.org\/10.1016\/j.micpro.2021.104412","journal-title":"Microprocess. Microsyst."},{"key":"5003_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2022.102930","volume":"134","author":"IA Khan","year":"2022","unstructured":"Khan, I.A., Keshk, M., Pi, D., Khan, N., Hussain, Y., Soliman, H.: Enhancing IIoT networks protection: a robust security model for attack detection in internet industrial control systems. Ad Hoc Netw. 134, 102930 (2022). https:\/\/doi.org\/10.1016\/j.adhoc.2022.102930","journal-title":"Ad Hoc Netw."},{"key":"5003_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109140","volume":"214","author":"RF Fouladi","year":"2022","unstructured":"Fouladi, R.F., Ermi\u015f, O., Anarim, E.: A DDoS attack detection and countermeasure scheme based on dwt and auto-encoder neural network for SDN. Comput. Netw. 214, 109140 (2022). https:\/\/doi.org\/10.1016\/j.comnet.2022.109140","journal-title":"Comput. Netw."},{"issue":"1","key":"5003_CR54","doi-asserted-by":"publisher","first-page":"140","DOI":"10.3390\/s22010140","volume":"22","author":"A Fatani","year":"2022","unstructured":"Fatani, A., Dahou, A., Al-Qaness, M.A., Lu, S., Abd Elaziz, M.: Advanced feature extraction and selection approach using deep learning and Aquila optimizer for IoT intrusion detection system. Sensors 22(1), 140 (2022). https:\/\/doi.org\/10.3390\/s22010140","journal-title":"Sensors"},{"key":"5003_CR55","doi-asserted-by":"publisher","first-page":"37131","DOI":"10.1109\/ACCESS.2023.3266979","volume":"11","author":"V Hnamte","year":"2023","unstructured":"Hnamte, V., Nhung-Nguyen, H., Hussain, J., Hwa-Kim, Y.: A novel two-stage deep learning model for network intrusion detection: LSTM-AE. IEEE Access 11, 37131\u201337148 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3266979","journal-title":"IEEE Access"},{"key":"5003_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2023.103445","volume":"74","author":"US Chanu","year":"2023","unstructured":"Chanu, U.S., Singh, K.J., Chanu, Y.J.: A dynamic feature selection technique to detect DDoS attack. J. Inf. Secur. Appl. 74, 103445 (2023). https:\/\/doi.org\/10.1016\/j.jisa.2023.103445","journal-title":"J. Inf. Secur. Appl."},{"key":"5003_CR57","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s11235-022-00981-4","volume":"82","author":"A Mishra","year":"2023","unstructured":"Mishra, A., Gupta, N., Gupta, B.: Defensive mechanism against DDoS attack based on feature selection and multi-classifier algorithms. Telecommun. Syst. 82, 229\u2013244 (2023). https:\/\/doi.org\/10.1007\/s11235-022-00981-4","journal-title":"Telecommun. Syst."},{"issue":"6","key":"5003_CR58","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). https:\/\/doi.org\/10.1109\/JBHI.2024.3352013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"5003_CR59","doi-asserted-by":"publisher","first-page":"9965","DOI":"10.1007\/s13369-021-06484-9","volume":"47","author":"A Prasad","year":"2022","unstructured":"Prasad, A., Chandra, S.: VMFCVD: an optimized framework to combat volumetric DDoS attacks using machine learning. Arab. J. Sci. Eng. 47, 9965\u20139983 (2022). https:\/\/doi.org\/10.1007\/s13369-021-06484-9","journal-title":"Arab. J. Sci. Eng."},{"key":"5003_CR60","doi-asserted-by":"publisher","unstructured":"Sharafaldin, I., Habibi Lashkari, A., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy\u2014ICISSP, INSTICC, SciTePress, 2018, pp. 108\u2013116 (2018). https:\/\/doi.org\/10.5220\/0006639801080116","DOI":"10.5220\/0006639801080116"},{"key":"5003_CR61","doi-asserted-by":"publisher","unstructured":"Powers, D.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37\u201363 (2011). https:\/\/doi.org\/10.48550\/arXiv.2010.16061","DOI":"10.48550\/arXiv.2010.16061"},{"key":"5003_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106738","volume":"86","author":"MV de Assis","year":"2020","unstructured":"de Assis, M.V., Carvalho, L.F., Rodrigues, J.J., Lloret, J., Proen\u00e7Sa, M.L., Jr.: Near real-time security system applied to SDN environments in IoT networks using convolutional neural network. Comput. Electr. Eng. 86, 106738 (2020). https:\/\/doi.org\/10.1016\/j.compeleceng.2020.106738","journal-title":"Comput. Electr. Eng."},{"issue":"11","key":"5003_CR63","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.3390\/electronics10111257","volume":"10","author":"MA Ferrag","year":"2021","unstructured":"Ferrag, M.A., Shu, L., Djallel, H., Choo, K.K.R.: Deep learning-based intrusion detection for distributed denial of service attack in agriculture 4.0. Electronics 10(11), 1257 (2021). https:\/\/doi.org\/10.3390\/electronics10111257","journal-title":"Electronics"},{"key":"5003_CR64","doi-asserted-by":"publisher","unstructured":"Jagtap, M.M., Saravanan, R.D.: Intelligent software defined networking: Long short term memory-graded rated unit enabled block-attack model to tackle distributed denial of service attacks. Trans. Emerg. Telecommun. Technol. 33(11) (2022). https:\/\/doi.org\/10.1002\/ett.4594","DOI":"10.1002\/ett.4594"},{"key":"5003_CR65","doi-asserted-by":"publisher","unstructured":"AL-Hawawreh, M., Moustafa, N., Sitnikova, E.: Identification of malicious activities in industrial internet of things based on deep learning models. J. Inf. Secur. Appl. 41, 1\u201311 (2018). https:\/\/doi.org\/10.1016\/j.jisa.2018.05.002","DOI":"10.1016\/j.jisa.2018.05.002"},{"issue":"6","key":"5003_CR66","doi-asserted-by":"publisher","DOI":"10.1002\/nem.2152","volume":"31","author":"M Marvi","year":"2021","unstructured":"Marvi, M., Arfeen, A., Uddin, R.: A generalized machine learning-based model for the detection of DDoS attacks. Int. J. Netw. Manag. 31(6), e2152 (2021). https:\/\/doi.org\/10.1002\/nem.2152","journal-title":"Int. J. Netw. Manag."},{"issue":"1","key":"5003_CR67","doi-asserted-by":"publisher","first-page":"20060","DOI":"10.1038\/s41598-024-70983-6","volume":"14","author":"AO Salau","year":"2024","unstructured":"Salau, A.O., Beyene, M.M.: Software defined networking based network traffic classification using machine learning techniques. Sci. Rep. 14(1), 20060 (2024). https:\/\/doi.org\/10.1038\/s41598-024-70983-6","journal-title":"Sci. Rep."},{"issue":"10","key":"5003_CR68","doi-asserted-by":"publisher","first-page":"6045","DOI":"10.3934\/era.2023308","volume":"31","author":"A Prasad","year":"2023","unstructured":"Prasad, A., Chandra, S., Atoum, I., Ahmad, N., Alqahhas, Y.: A collaborative prediction approach to defend against amplified reflection and exploitation attacks. Electron. Res. Arch. 31(10), 6045\u20136070 (2023). https:\/\/doi.org\/10.3934\/era.2023308","journal-title":"Electron. Res. Arch."},{"key":"5003_CR69","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.comcom.2024.04.001","volume":"221","author":"UH Garba","year":"2024","unstructured":"Garba, U.H., Toosi, A.N., Pasha, M.F., Khan, S.: SDN-based detection and mitigation of DDoS attacks on smart homes. Comput. Commun. 221, 29\u201341 (2024). https:\/\/doi.org\/10.1016\/j.comcom.2024.04.001","journal-title":"Comput. Commun."},{"issue":"4","key":"5003_CR70","doi-asserted-by":"publisher","first-page":"538","DOI":"10.3390\/network3040024","volume":"3","author":"MA Setitra","year":"2023","unstructured":"Setitra, M.A., Fan, M., Agbley, B.L.Y., Bensalem, Z.E.A.: Optimized MLP-CNN model to enhance detecting DDoS attacks in SDN environment. Network 3(4), 538\u2013562 (2023). https:\/\/doi.org\/10.3390\/network3040024","journal-title":"Network"},{"issue":"4","key":"5003_CR71","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.1109\/TCCN.2022.3186331","volume":"8","author":"MSE Sayed","year":"2022","unstructured":"Sayed, M.S.E., 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. Cogn. Commun. Netw. 8(4), 1862\u20131880 (2022). https:\/\/doi.org\/10.1109\/TCCN.2022.3186331","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"5003_CR72","doi-asserted-by":"publisher","first-page":"51630","DOI":"10.1109\/ACCESS.2024.3384398","volume":"12","author":"AA Alashhab","year":"2024","unstructured":"Alashhab, A.A., Zahid, M.S., Isyaku, B., Elnour, A.A., Nagmeldin, W., Abdelmaboud, A., Abdullah, T.A.A., Maiwada, U.D.: Enhancing DDoS attack detection and mitigation in SDN using an ensemble online machine learning model. IEEE Access 12, 51630\u201351649 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3384398","journal-title":"IEEE Access"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05003-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-05003-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05003-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T20:21:09Z","timestamp":1756930869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-05003-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":72,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["5003"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-05003-3","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"17 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 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 that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"347"}}