A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Neural Networks: A Comprehensive Foundation

Haykin, S. (1998) · Cited by 30,070

Data mining: concepts and techniques

Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665

Statistical Learning Theory

Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225

The Elements of Statistical Learning

Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

Wang, C., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y., Chen, Y., Haas, H., et al. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3249835 · Cited by 2,613

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. A. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2926625 · Cited by 1,378

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, K.-C., & Hanzo, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2965856 · Cited by 568

Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

Jahanbakht, M., Xiang, W., Hanzo, L., & Azghadi, M. R. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3053118 · Cited by 474

A survey on the adoption of blockchain in IoT: challenges and solutions

Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2021) · Blockchain: Research and Applications · doi:10.1016/j.bcra.2021.100006 · Cited by 365

Internet of underwater things and big marine data analytics – a comprehensive survey

Jahanbakht, M., Xiang, W., Hanzo, L., & Azghadi, M. R. (2021) · ResearchOnline at James Cook University (James Cook University) · Cited by 317

Blockchain and Machine Learning for Communications and Networking Systems

Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975911 · Cited by 332

Detection and mitigation of DDoS attacks in SDN: A comprehensive review, research challenges and future directions

Singh, J., & Behal, S. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100279 · Cited by 254

Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

Bhayo, J., Shah, S. A., Hameed, S., Ahmed, A., Nasir, J. A., & Draheim, D. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106432 · Cited by 233

TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT

Lin, K., Xu, X., & Gao, H. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107974 · Cited by 226

Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

Shafiq, M., Tian, Z., Bashir, A. K., Jolfaei, A., & Yu, X. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102177 · Cited by 232

Routing optimization meets Machine Intelligence: A perspective for the future network

Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., & Xu, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.06.093 · Cited by 215

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Mehrabi, M., You, D., Latzko, V., Salah, H., Martin Reisslein, B., & Fitzek, F. H. P. (2019) · IEEE Access · doi:10.1109/access.2019.2953172 · Cited by 203

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Oubbati, O. S., Atiquzzaman, M., Ahanger, T. A., & Ibrahim, A. (2020) · IEEE Access · doi:10.1109/access.2020.2994494 · Cited by 194

Zero Touch Management: A Survey of Network Automation Solutions for 5G and 6G Networks

Coronado, E., Behravesh, R., Subramanya, T., Fernández–Fernández, A., Siddiqui, M. S., Costa‐Pérez, X., & Riggio, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3212586 · Cited by 203

A Survey on Machine Learning Techniques for Routing Optimization in SDN

Amin, R., Rojas, E., Aqdus, A., Ramzan, S., Casillas-Pérez, D., & Arco, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3099092 · Cited by 198

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Zhao, Y., Li, Y., Zhang, X., Geng, G., Zhang, W., & Sun, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2928564 · Cited by 176

A comprehensive survey of load balancing techniques in software-defined network

Hamdan, M., Hassan, E., Abdelaziz, A., Abdallah, A. E., Mohammed, B., Khan, S., Vasilakos, A. V., & Marsono, M. N. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102856 · Cited by 176

AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges

Al-Doghman, F., Moustafa, N., Khalil, I., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3155447 · Cited by 179

Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities

Zhang, S., & Zhu, D. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107556 · Cited by 176

Machine learning for intelligent optical networks: A comprehensive survey

Gu, R., Yang, Z., & Ji, Y. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102576 · Cited by 184

Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey

Shuja, J., Bilal, K., Alasmary, W., Sinky, H., & Alanazi, E. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103005 · Cited by 162

Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey

Sánchez, J. A. H., Casilimas, K., & Rendón, O. M. C. (2022) · Sensors · doi:10.3390/s22083031 · Cited by 165

Throughput Prediction using Machine Learning in LTE and 5G Networks

Minovski, D., Ögren, N., Åhlund, C., & Mitra, K. (2021) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2021.3099397 · Cited by 135

Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy

Soni, D., & Kumar, N. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103419 · Cited by 144

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ahmad, I., Shahabuddin, S., Malik, H. H., Harjula, E., Leppänen, T., Lovén, L., Anttonen, A., Sodhro, A. H., Alam, M. M., Juntti, M. J., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3041765 · Cited by 140

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Saif, W. S., Esmail, M. A., Ragheb, A. M., Alshawi, T. A., & Alshebeili, S. A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3018494 · Cited by 135

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

Xiao, Y., Liu, J., Wu, J., & Ansari, N. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3102580 · Cited by 146

FMDADM: A Multi-Layer DDoS Attack Detection and Mitigation Framework Using Machine Learning for Stateful SDN-Based IoT Networks

Khedr, W. I., Gouda, A. E., & Mohamed, E. R. (2023) · IEEE Access · doi:10.1109/access.2023.3260256 · Cited by 144

Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey

Isyaku, B., Zahid, M. S. M., Kamat, M. B., Bakar, K. A., & Ghaleb, F. A. A. (2020) · Future Internet · doi:10.3390/fi12090147 · Cited by 127

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Bouraqia, K., Sabir, E., Sadik, M., & Ladid, L. (2020) · IEEE Access · doi:10.1109/access.2020.2965099 · Cited by 126

Adaptable and Data-Driven Softwarized Networks: Review, Opportunities, and Challenges

Kellerer, W., Kalmbach, P., Blenk, A., Basta, A., Martin Reisslein, B., & Schmid, S. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2895553 · Cited by 123

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

Sellami, B., Hakiri, A., Yahia, S. B., & Berthou, P. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108957 · Cited by 120

VARMAN: Multi-plane security framework for software defined networks

Krishnan, P., Duttagupta, S., & Achuthan, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.09.014 · Cited by 109

Security Control and Data Planes of SDN: A Comprehensive Review of Traditional, AI, and MTD Approaches to Security Solutions

Hirsi, A., Audah‏, L., Salh, A., Alhartomi, M. A., Rasheed, H., Ahmed, S., & Tahir, A. (2024) · IEEE Access · doi:10.1109/access.2024.3393548 · Cited by 108

Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning

Raikar, M. M., Meena, S. M., Mulla, M. M., Shetti, N. S., & Karanandi, M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.299 · Cited by 107

Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future Directions

Aouedi, O., Le, V. A., Piamrat, K., & Ji, Y. (2025) · ACM Computing Surveys · doi:10.1145/3703447 · Cited by 132

Emerging DDoS attack detection and mitigation strategies in software-defined networks: Taxonomy, challenges and future directions

Valdovinos, I. A., Pérez-Díaz, J. A., Choo, K. R., & Botero, J. F. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103093 · Cited by 108

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

Troia, S., Sapienza, F., Vare, L., & Maier, G. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041385 · Cited by 95

Toward 6G: An Overview of the Next Generation of Intelligent Network Connectivity

Tera, S. P., Chinthaginjala, R., Pau, G., & Kim, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3523327 · Cited by 113

6G: The Intelligent Network of Everything

Pennanen, H., Hänninen, T., Tervo, O., Tölli, A., & Latva‐aho, M. (2024) · IEEE Access · doi:10.1109/access.2024.3521579 · Cited by 117

A Comparative Study of Traffic Classification Techniques for Smart City Networks

AlZoman, R. M., & Alenazi, M. J. F. (2021) · Sensors · doi:10.3390/s21144677 · Cited by 101

The application of Software Defined Networking on securing computer networks: A survey

Sahay, R., Meng, W., & Jensen, C. D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.01.019 · Cited by 97

From statistical‐ to machine learning‐based network traffic prediction

Lohrasbinasab, I., Shahraki, A., Taherkordi, A., & Jurcut, A. D. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4394 · Cited by 113

A Survey of the Main Security Issues and Solutions for the SDN Architecture

Jiménez, M. B., Fernández, D., Rivadeneira, J. E., Bellido, L., & Cardenas, A. J. A. (2021) · IEEE Access · doi:10.1109/access.2021.3109564 · Cited by 94

Machine Learning: Models, Challenges, and Research Directions

Khoei, T. T., & Kaabouch, N. (2023) · Future Internet · doi:10.3390/fi15100332 · Cited by 107

Artificial intelligence advances in anomaly detection for telecom networks

Edozie, E., Shuaibu, A. N., Sadiq, B. O., & Ukagwu, K. J. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11108-x · Cited by 93

QR-SDN: Towards Reinforcement Learning States, Actions, and Rewards for Direct Flow Routing in Software-Defined Networks

Rischke, J., Sossalla, P., Salah, H., Fitzek, F. H. P., & Martin Reisslein, B. (2020) · IEEE Access · doi:10.1109/access.2020.3025432 · Cited by 95

Artificial intelligence based load balancing in SDN: A comprehensive survey

Alhilali, A. H., & Montazerolghaem, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100814 · Cited by 89

Intelligent Radio Signal Processing: A Survey

Pham, Q., Nguyen, N. T., Huynh‐The, T., Le, L. B., Lee, K., & Hwang, W. (2021) · IEEE Access · doi:10.1109/access.2021.3087136 · Cited by 97

A New Traffic Prediction Algorithm to Software Defined Networking

Wang, Y., Jiang, D., Huo, L., & Zhao, Y. (2019) · Mobile Networks and Applications · doi:10.1007/s11036-019-01423-3 · Cited by 85

Software-defined DDoS detection with information entropy analysis and optimized deep learning

Liu, Y., Zhi, T., Shen, M., Wang, L., Li, Y., & Wan, M. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.009 · Cited by 87

Machine Learning and Deep Learning Based Traffic Classification and Prediction in Software Defined Networking

Mohammed, A. R., Mohammed, S., & Shirmohammadi, S. (2019) · IEEE International Symposium on Measurements & Networking (M&N) · doi:10.1109/iwmn.2019.8805044 · Cited by 83

DeepGuard: Efficient Anomaly Detection in SDN With Fine-Grained Traffic Flow Monitoring

Phan, T. V., Nguyen, T. G., Dao, N., Hương, T. T., Thanh, N. H., & Bauschert, T. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3004415 · Cited by 80

Securing massive IoT in 6G: Recent solutions, architectures, future directions

Alotaibi, A. I., & Barnawi, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100715 · Cited by 81

Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges

Tang, Q., Yu, F. R., Xie, R., Boukerche, A., Huang, T., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3175453 · Cited by 81

Active Learning for Network Traffic Classification: A Technical Study

Shahraki, A., Abbasi, M., Taherkordi, A., & Jurcut, A. D. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3119062 · Cited by 79

Fog computing systems: State of the art, research issues and future trends, with a focus on resilience

Moura, J., & Hutchison, D. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102784 · Cited by 79

Machine Learning Threatens 5G Security

Suomalainen, J., Juhola, A., Shahabuddin, S., Mämmelä, A., & Ahmad, I. (2020) · IEEE Access · doi:10.1109/access.2020.3031966 · Cited by 80

A Survey on Deep Neural Network Compression: Challenges, Overview, and SolutionsPreprint

Mishra, R., Gupta, H. P., & Dutta, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03954 · Cited by 114

Chaotic characteristic analysis of network traffic time series at different time scales

Tian, Z. (2019) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2019.109412 · Cited by 72

Impacts of blockchain in software‐defined Internet of Things ecosystem with Network Function Virtualization for smart applications: Present perspectives and future directions

Rahman, A., Islam, J., Kundu, D., Karim, M. R., Rahman, Z., Band, S. S., Sookhak, M., Tiwari, P., & Kumar, N. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5429 · Cited by 74

A Promising Integration of SDN and Blockchain for IoT Networks: A Survey

Turner, S. W., Karakuş, M., Güler, E., & Uludag, S. (2023) · IEEE Access · doi:10.1109/access.2023.3260777 · Cited by 76

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Ridwan, M. A., Radzi, N. A. M., Abdullah, F., & Jalil, Y. E. (2021) · IEEE Access · doi:10.1109/access.2021.3069210 · Cited by 76

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Ahmed, N., Ngadi, M. A., Sharif, J. M., Hussain, S., Uddin, M., Rathore, M. S., Iqbal, J., Abdelhaq, M., Alsaqour, R. A., Ullah, S. S., et al. (2022) · Sensors · doi:10.3390/s22207896 · Cited by 69

Collective Deep Reinforcement Learning for Intelligence Sharing in the Internet of Intelligence-Empowered Edge Computing

Tang, Q., Xie, R., Yu, F. R., Chen, T., Zhang, R., Huang, T., & Liu, Y. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3199812 · Cited by 68

Overview on routing and resource allocation based machine learning in optical networks

Zhang, Y., Xin, J., Li, X., & Huang, S. (2020) · Optical Fiber Technology · doi:10.1016/j.yofte.2020.102355 · Cited by 68

Large Language Models Meet Next-Generation Networking Technologies: A Review

Hang, C. N., Yu, P.-D., Morabito, R., & Tan, C. W. (2024) · Future Internet · doi:10.3390/fi16100365 · Cited by 75

DistB-Condo: Distributed Blockchain-Based IoT-SDN Model for Smart Condominium

Rahman, A., Islam, M. J., Rahman, Z., Reza, M. M., Anwar, A., Mahmud, M. A. P., Nasir, M. K., & Noor, R. M. (2020) · IEEE Access · doi:10.1109/access.2020.3039113 · Cited by 66

Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects

Wang, C., Chen, J., Yang, Y., Ma, X., & Liu, J. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.07.009 · Cited by 68

Software-Defined Networking: Categories, Analysis, and Future Directions

Hussain, M., Shah, N., Amin, R., Alshamrani, S. S., Alotaibi, A., & Raza, S. M. (2022) · Sensors · doi:10.3390/s22155551 · Cited by 64

Flowrest: Practical Flow-Level Inference in Programmable Switches with Random Forests

Akem, A. T., Gucciardo, M., & Fiore, M. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10229100 · Cited by 63

Towards Blockchain-Based Software-Defined Networking: Security Challenges and Solutions

Li, W., Meng, W., Liu, Z., & Man-Ho, A. U. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019ini0002 · Cited by 69

VPNFilter Malware Analysis on Cyber Threat in Smart Home Network

Sicato, J. C. S., Sharma, P. K., Loia, V., & Park, J. H. (2019) · Applied Sciences · doi:10.3390/app9132763 · Cited by 62

Recognition of DDoS attacks on SD-VANET based on combination of hyperparameter optimization and feature selection

Türkoğlu, M., Polat, H., Koçak, C., & Polat, O. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117500 · Cited by 62

Transportation Internet: A Sustainable Solution for Intelligent Transportation Systems

Li, H., Chen, Y., Li, K., Wang, C., & Chen, B. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3270749 · Cited by 62

Evaluation of Machine Learning Techniques for Security in SDN

Ahmad, A., Harjula, E., Ylianttila, M., & Ahmad, I. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367477 · Cited by 61

Handover management over dual connectivity in 5G technology with future ultra-dense mobile heterogeneous networks: A review

Khan, S. A., Shayea, I. A. M., Ergen, M., & Mohamad, H. (2022) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2022.101172 · Cited by 60

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

Cao, X., Yang, B. F., Wang, K., Li, X., Yu, Z., Yuen, C., Zhang, Y., & Han, Z. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3417332 · Cited by 69

A Survey of Fault Management in Network Virtualization Environments: Challenges and Solutions

Cherrared, S., Imadali, S., Fabre, É., Gößler, G., & Yahia, I. G. B. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2948420 · Cited by 62

Modeling Quality of IoT Experience in Autonomous Vehicles

Minovski, D., Ahlund, C., & Mitra, K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2975418 · Cited by 59

Flow-Aware Elephant Flow Detection for Software-Defined Networks

Hamdan, M., Mohammed, B., Humayun, U., Abdelaziz, A., Khan, S., Ali, M. A., Imran, M. A., & Marsono, M. N. (2020) · IEEE Access · doi:10.1109/access.2020.2987977 · Cited by 58

Inertial Sensing Meets Machine Learning: Opportunity or Challenge?

Li, Y., Chen, R., Niu, X., Zhuang, Y., Gao, Z., Hu, X., & El‐Sheimy, N. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3097385 · Cited by 67

A Survey on Machine Learning Software-Defined Wireless Sensor Networks (ML-SDWSNs): Current Status and Major Challenges

Jurado-Lasso, F. F., Marchegiani, L., Jurado, J. F., Abu-Mahfouz, A. M. I., & Fafoutis, X. (2022) · IEEE Access · doi:10.1109/access.2022.3153521 · Cited by 64

QoS Aware Transcoding for Live Streaming in Edge-Clouds Aided HetNets: An Enhanced Actor-Critic Approach

Zhang, Z., Wang, R., Yu, F. R., Fu, F., & Yan, Q. (2019) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2942629 · Cited by 56

Procedures, Criteria, and Machine Learning Techniques for Network Traffic Classification: A Survey

Sheikh, M. S., & Peng, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3181135 · Cited by 65

An In-Depth Survey on Virtualization Technologies in 6G Integrated Terrestrial and Non-Terrestrial Networks

Ammar, S., Lau, C. P., & Shihada, B. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3414622 · Cited by 71

Many-Objective Optimization-Based Content Popularity Prediction for Cache-Assisted Cloud–Edge–End Collaborative IoT Networks

Hu, Z., Fang, C., Wang, Z., Tseng, S., & Dong, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3290793 · Cited by 55

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

Abubakar, A. I., Omeke, K. G., Öztürk, M., Hussain, S., & Imran, M. A. (2020) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2020.575065 · Cited by 55

A novel mechanism to handle address spoofing attacks in SDN based IoT

Aldabbas, H., & Amin, R. (2021) · Cluster Computing · doi:10.1007/s10586-021-03309-0 · Cited by 54

A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions

Makris, I., Karampasi, A. S., Grammatikis, P. I. R., Episkopos, N., Iturbe, E., Rios, E., Piperigkos, N., Lalos, A. S., Xenakis, C., Λάγκας, Θ., et al. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100717 · Cited by 67

A survey on space-aerial-terrestrial integrated 5G networks

Zhang, S., Zhu, D., & Wang, Y. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107212 · Cited by 53

IHSF: An Intelligent Solution for Improved Performance of Reliable and Time-Sensitive Flows in Hybrid SDN-Based FC IoT Systems

Ibrar, M., Wang, L., Muntean, G., Chen, J., Shah, N., & Akbar, A. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3024560 · Cited by 53

A survey on stateful data plane in software defined networks

Zhang, X., Cui, L., Wei, K., Tso, F. P., Ji, Y., & Jia, W. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107597 · Cited by 52

DeepAir: Deep Reinforcement Learning for Adaptive Intrusion Response in Software-Defined Networks

Phan, T. V., & Bauschert, T. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3158468 · Cited by 51

Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications

Tam, P., Corrado, R., Eang, C., & Kim, S. (2023) · Applied Sciences · doi:10.3390/app13053083 · Cited by 53

PrePass-Flow: A Machine Learning based technique to minimize ACL policy violation due to links failure in hybrid SDN

Ibrar, M., Wang, L., Muntean, G., Akbar, A., Shah, N., & Malik, K. R. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107706 · Cited by 51

GraphNET: Graph Neural Networks for routing optimization in Software Defined Networks

Swaminathan, A., Chaba, M., Sharma, D. K., & Ghosh, U. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.07.025 · Cited by 50

Towards security automation in Software Defined Networks

Yungaicela-Naula, N. M., Vargas‐Rosales, C., Pérez-Díaz, J. A., & Zareei, M. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.11.014 · Cited by 50

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag, R. H., Abdalzaher, M. S., Elsayed, H. A. E. A., Sobh, M., Krichen, M., & Salim, M. M. (2024) · Electronics · doi:10.3390/electronics13061108 · Cited by 50

A SURVEY ON MACHINE LEARNING ALGORITHMS FOR SOFTWARE-DEFINED NETWORKS

Kanivets, Z., & Vybornova, A. (2021) · Telecom IT · doi:10.31854/2307-1303-2021-9-3-11-21

Control Traffic in SDN Systems by using Machine Learning techniques: Review

Askar, S. K., Hussein, D. H., Ibrahim, M., & Mohammed, M. A. (2025) · International Journal of Research and Applied Technology · doi:10.34010/injuratech.v5i1.15764

A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Zhao, Y., Li, Y., Zhang, X., Geng, G., Zhang, W., & Sun, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2928564 · Cited by 176

Exploring the Synergy: A Review of Machine Learning Techniques in Software Defined Networking (SDN)

Muheden, K., Othman, R. N., Hawezi, R. S., Abdalwahid, S. M. J., Mustafa, O. S., & Kareem, S. W. (2024) · ITM Web of Conferences · doi:10.1051/itmconf/20246401016

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag, R. H., Abdalzaher, M. S., Elsayed, H. A. E. A., Sobh, M., Krichen, M., & Salim, M. M. (2024) · Electronics · doi:10.3390/electronics13061108 · Cited by 50