{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:32:50Z","timestamp":1776940370444,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MECI","award":["UID\/50008"],"award-info":[{"award-number":["UID\/50008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Internet has been vulnerable to several attacks as it has expanded, including spoofing, viruses, malicious code attacks, and Distributed Denial of Service (DDoS). The three main types of attacks most frequently reported in the current period are viruses, DoS attacks, and DDoS attacks. Advanced DDoS and DoS attacks are too complex for traditional security solutions, such as intrusion detection systems and firewalls, to detect. The combination of machine learning methods with AI-based machine learning has led to the introduction of several novel attack detection systems. Due to their remarkable performance, machine learning models, in particular, have been essential in identifying DDoS attacks. However, there is a considerable gap in the work on real-time detection of such attacks. This study uses Mininet with the POX Controller to simulate an environment to detect DDoS attacks in real-time settings. The CICDDoS2019 dataset identifies and classifies such attacks in the simulated environment. In addition, a virtual software-defined network (SDN) is used to collect network information from the surrounding area. When an attack occurs, the pre-trained models are used to analyze the traffic and predict the attack in real-time. The performance of the proposed methodology is evaluated based on two metrics: accuracy and detection time. The results reveal that the proposed model achieves an accuracy of 99% within 1 s of the detection time.<\/jats:p>","DOI":"10.3390\/s25061905","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T06:10:53Z","timestamp":1742364653000},"page":"1905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Enhancing Security in 5G Edge Networks: Predicting Real-Time Zero Trust Attacks Using Machine Learning in SDN Environments"],"prefix":"10.3390","volume":"25","author":[{"given":"Fiza","family":"Ashfaq","sequence":"first","affiliation":[{"name":"Department of Computer Science, UMT Sialkot Campus, KUST, Sialkot 51040, Pakistan"}]},{"given":"Muhammad","family":"Wasim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UMT Sialkot Campus, KUST, Sialkot 51040, Pakistan"}]},{"given":"Mumtaz Ali","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3914-2503","authenticated-orcid":false,"given":"Abdul","family":"Ahad","sequence":"additional","affiliation":[{"name":"School of Software, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"Department of Electronics and Communication Engineering, Istanbul Technical University (ITU), Maslak, Istanbul 34469, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-6762","authenticated-orcid":false,"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda, Universidade de Aveiro, 3810-193 \u00c1gueda, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7746","DOI":"10.1109\/JIOT.2021.3114270","article-title":"Centralized and Distributed Intrusion Detection for Resource-Constrained Wireless SDN Networks","volume":"9","author":"Segura","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.comcom.2021.02.013","article-title":"Sieve: A flow scheduling framework in SDN based data center networks","volume":"171","author":"Zaher","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Becerra-Suarez, F.L., Fern\u00e1ndez-Roman, I., and Forero, M.G. (2024). Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing. Mathematics, 12.","DOI":"10.3390\/math12091294"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.37868\/sei.v3i1.124","article-title":"Survey of DoS\/DDoS attacks in IoT","volume":"3","author":"Khader","year":"2021","journal-title":"Sustain. Eng. Innov."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s10922-020-09580-7","article-title":"An SDN-assisted defense mechanism for the shrew DDoS attack in a cloud computing environment","volume":"29","author":"Agrawal","year":"2021","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ramzan, M., Shoaib, M., Altaf, A., Arshad, S., Iqbal, F., Castilla, \u00c1.K., and Ashraf, I. (2023). Distributed denial of service attack detection in network traffic using deep learning algorithm. Sensors, 23.","DOI":"10.3390\/s23208642"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"146810","DOI":"10.1109\/ACCESS.2021.3123791","article-title":"AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification","volume":"9","author":"Wei","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1007\/s11277-020-07812-2","article-title":"A systematic review of quality of services (QoS) in software defined networking (SDN)","volume":"116","author":"Keshari","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_9","first-page":"17","article-title":"Implementing a zero trust architecture","volume":"2020","author":"Kerman","year":"2020","journal-title":"Natl. Inst. Stand. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ashfaq, F., Ahad, A., Hussain, M., Shayea, I., and Pires, I.M. (2024, January 26\u201328). Enhancing Zero Trust Security in Edge Computing Environments: Challenges and Solutions. Proceedings of the World Conference on Information Systems and Technologies, Lodz, Poland.","DOI":"10.1007\/978-3-031-60221-4_41"},{"key":"ref_11","first-page":"59","article-title":"A survey on software defined wide area network","volume":"17","author":"Badotra","year":"2020","journal-title":"Int. J. Appl. Sci. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Alzahrani, R.J., and Alzahrani, A. (2021). Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic. Electronics, 10.","DOI":"10.3390\/electronics10232919"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gupta, N., Maashi, M.S., Tanwar, S., Badotra, S., Aljebreen, M., and Bharany, S. (2022). A comparative study of software defined networking controllers using mininet. Electronics, 11.","DOI":"10.3390\/electronics11172715"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8538","DOI":"10.1080\/03772063.2024.2394606","article-title":"Performance Analysis of an OpenFlow-Enabled Network with POX, Ryu, and ODL Controllers","volume":"70","author":"Das","year":"2024","journal-title":"IETE J. Res."},{"key":"ref_15","first-page":"215","article-title":"Intrusion detection model using naive bayes and deep learning technique","volume":"17","author":"Tabash","year":"2020","journal-title":"Int. Arab J. Inf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.future.2021.03.011","article-title":"DoS and DDoS attacks in Software Defined Networks: A survey of existing solutions and research challenges","volume":"122","author":"Eliyan","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104018","DOI":"10.1016\/j.jnca.2024.104018","article-title":"Clone node detection in static wireless sensor networks: A hybrid approach","volume":"232","author":"Numan","year":"2024","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_18","first-page":"1618","article-title":"Artificial intelligence and participatory leadership: The role of technological transformation in business management and its impact on employee participation","volume":"6","author":"Sarioguz","year":"2024","journal-title":"Int. Res. J. Mod. Eng. Technol. Sci."},{"key":"ref_19","unstructured":"Alpaydin, E. (2020). Introduction to Machine Learning, MIT Press."},{"key":"ref_20","unstructured":"Abou El Houda, Z. (2021). Security Enforcement Through Software Defined Networks (SDN). [Ph.D. Thesis, Universit\u00e9 de Montr\u00e9al]."},{"key":"ref_21","unstructured":"Taj, R. (2020). A Machine Learning Framework for Host Based Intrusion Detection Using System Call Abstraction, Dalhousie University."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"923","DOI":"10.32604\/iasc.2022.024668","article-title":"Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network","volume":"33","author":"Alanazi","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hussain, F., Abbas, S.G., Husnain, M., Fayyaz, U.U., Shahzad, F., and Shah, G.A. (2020, January 5\u20137). IoT DoS and DDoS attack detection using ResNet. Proceedings of the 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan.","DOI":"10.1109\/INMIC50486.2020.9318216"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jiyad, Z.M., Al Maruf, A., Haque, M.M., Gupta, M.S., Ahad, A., and Aung, Z. (2024, January 18\u201320). DDoS Attack Classification Leveraging Data Balancing and Hyperparameter Tuning Approach Using Ensemble Machine Learning with XAI. Proceedings of the 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India.","DOI":"10.1109\/ICPC2T60072.2024.10475035"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3779","DOI":"10.1109\/TNNLS.2021.3121870","article-title":"Deep Reinforcement Learning for Cyber Security","volume":"34","author":"Nguyen","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1049\/iet-net.2017.0212","article-title":"Machine learning-based IDS for software-defined 5G network","volume":"7","author":"Li","year":"2018","journal-title":"IET Netw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nanda, S., Zafari, F., DeCusatis, C., Wedaa, E., and Yang, B. (2016, January 7\u201310). Predicting network attack patterns in SDN using machine learning approach. Proceedings of the 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), Palo Alto, CA, USA.","DOI":"10.1109\/NFV-SDN.2016.7919493"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Polat, H., Polat, O., and Cetin, A. (2020). Detecting DDoS attacks in software-defined networks through feature selection methods and machine learning models. Sustainability, 12.","DOI":"10.3390\/su12031035"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e1674","DOI":"10.7717\/peerj-cs.1674","article-title":"An intelligent zero trust secure framework for software defined networking","volume":"9","author":"Guo","year":"2023","journal-title":"PeerJ Comput. Sci."},{"key":"ref_30","first-page":"24","article-title":"DDoS detection using machine learning techniques","volume":"4","author":"Amrish","year":"2022","journal-title":"J. IoT Soc. Mob. Anal. Cloud"},{"key":"ref_31","first-page":"192","article-title":"Case Study of Cybercrime Implementation in Technology Development","volume":"2","author":"Maulana","year":"2024","journal-title":"Interdiscip. J. Adv. Res. Innov."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, P., Chao, K.M., Lin, H.C., Lin, W.H., and Lo, C.C. (2016, January 4\u20136). An Efficient Flow Control Approach for SDN-Based Network Threat Detection and Migration Using Support Vector Machine. Proceedings of the 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE), Macau, China.","DOI":"10.1109\/ICEBE.2016.020"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e232","DOI":"10.1002\/itl2.232","article-title":"Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network","volume":"5","author":"Rawat","year":"2022","journal-title":"Internet Technol. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/ACCESS.2019.2963077","article-title":"DDoS Attack Detection Method Based on Improved KNN With the Degree of DDoS Attack in Software-Defined Networks","volume":"8","author":"Dong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.47672\/ajce.2120","article-title":"Enhanced Attacks Detection and Mitigation in Software Defined Networks","volume":"7","author":"Forbacha","year":"2024","journal-title":"Am. J. Comput. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Han, D., Li, H., Fu, X., and Zhou, S. (2024). Traffic Feature Selection and Distributed Denial of Service Attack Detection in Software-Defined Networks Based on Machine Learning. Sensors, 24.","DOI":"10.3390\/s24134344"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Siva reddy, S.V., and Saravanan, S. (2020, January 10\u201312). Performance evaluation of classification algorithms in the design of Apache Spark based intrusion detection system. Proceedings of the 2020 5th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES48766.2020.9138066"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Priya, S.S., Sivaram, M., Yuvaraj, D., and Jayanthiladevi, A. (2020, January 12\u201314). Machine learning based DDoS detection. Proceedings of the 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India.","DOI":"10.1109\/ESCI48226.2020.9167642"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ujjan, R.M.A., Pervez, Z., Dahal, K., Khan, W.A., Khattak, A.M., and Hayat, B. (2021). Entropy based features distribution for anti-DDoS model in SDN. Sustainability, 13.","DOI":"10.3390\/su13031522"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gadze, J.D., Bamfo-Asante, A.A., Agyemang, J.O., Nunoo-Mensah, H., and Opare, K.A.B. (2021). An investigation into the application of deep learning in the detection and mitigation of DDOS attack on SDN controllers. Technologies, 9.","DOI":"10.3390\/technologies9010014"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103108","DOI":"10.1016\/j.jnca.2021.103108","article-title":"Automated DDOS attack detection in software defined networking","volume":"187","author":"Ahuja","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16062","DOI":"10.1109\/ACCESS.2021.3051074","article-title":"Deep belief network integrating improved kernel-based extreme learning machine for network intrusion detection","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1007\/s11227-020-03323-w","article-title":"The DDoS attacks detection through machine learning and statistical methods in SDN","volume":"77","author":"Soltanaghaei","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sharafaldin, I., Lashkari, A.H., Hakak, S., and Ghorbani, A.A. (2019, January 1\u20133). Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy. Proceedings of the 2019 International Carnahan Conference on Security Technology (ICCST), Chennai, India.","DOI":"10.1109\/CCST.2019.8888419"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.procs.2021.02.003","article-title":"A DDoS attack mitigation framework for IoT networks using fog computing","volume":"182","author":"Lawal","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ashraf, U., Sharif, H., Usman, S., and Hasnain, M. (2024). A Machine Learning Based Approach for the Detection of DDoS Attacks on Internet of Things Using CICDDoS2019 Dataset-PortMap. Lahore Garrison Univ. Res. J. Comput. Sci. Inf. Technol., 8.","DOI":"10.54692\/lgurjcsit.2024.082569"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kousar, H., Mulla, M.M., Shettar, P., and Narayan, D. (2021, January 18\u201319). Detection of DDoS attacks in software defined network using decision tree. Proceedings of the 2021 10th IEEE international conference on Communication Systems and Network Technologies (CSNT), Bhopal, Indi.","DOI":"10.1109\/CSNT51715.2021.9509634"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/6\/1905\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:56:20Z","timestamp":1760028980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/6\/1905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,19]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["s25061905"],"URL":"https:\/\/doi.org\/10.3390\/s25061905","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,19]]}}}