Classification of the Weighted Network Traffic Approach Using an Optimized Deep Neural Network Algorithm
Received: 15 December 2024 | Revised: 21 February 2025 | Accepted: 6 March 2025 | Online: 2 August 2025
Corresponding author: Rasool Altaee
Abstract
In the context of increasing network traffic type complexity, traditional traffic classification methods face significant challenges due to the nature of resource constraints, which handle massive amounts of traffic with limited processing resources. The complexity of network services is also a contributing factor, as the diversity of applications leads to a failure to adapt to new services and applications. This, in turn, results in to inaccuracies in identifying traffic types. In this paper, Weighted Network Traffic (WNT) is proposed as a means of leveraging an optimized Deep Neural Network (DNN) algorithm to enhance classification accuracy and efficiency for the entire network performance. The proposed system integrates a robust preprocessing method based on feature engineering and class reduction processes applied on IP Network Traffic Flows Labeled with 75 Apps and CICIDS2017 datasets. In the proposed WNT approach, traffic is categorized based on bandwidth metrics into three weight categories: high, moderate, and low. The optimized DNN model was evaluated using three train-test splits: 60-40, 70-30 and 80-20. The best results were achieved with the IP Network Traffic Flows Labeled with 75 Apps dataset using the 60-40 split, with a classification accuracy of 99.89%, a low loss function of 0.0043, and a and a model build time of 1 hour and 11 minutes. This performance surpasses that of the CICIDS2017 dataset and other state-of-the-art methods.
Keywords:
Deep Neural Network (DNN), network traffic, classification, Weighted Network Traffic (WNT)Downloads
References
S. Zahoor and R. N. Mir, "Resource management in pervasive Internet of Things: A survey," Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 8, pp. 921–935, Oct. 2021. DOI: https://doi.org/10.1016/j.jksuci.2018.08.014
S. Jagtap, G. Garcia-Garcia, and S. Rahimifard, "Optimisation of the resource efficiency of food manufacturing via the Internet of Things," Computers in Industry, vol. 127, May 2021, Art. no. 103397. DOI: https://doi.org/10.1016/j.compind.2021.103397
W. A. Jabbar, T. Subramaniam, A. E. Ong, M. I. Shu'Ib, W. Wu, and M. A. de Oliveira, "LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring," Internet of Things, vol. 19, Aug. 2022, Art. no. 100540. DOI: https://doi.org/10.1016/j.iot.2022.100540
S. S. Gill et al., "AI for next generation computing: Emerging trends and future directions," Internet of Things, vol. 19, Aug. 2022, Art. no. 100514. DOI: https://doi.org/10.1016/j.iot.2022.100514
B. B. Sinha and R. Dhanalakshmi, "Recent advancements and challenges of Internet of Things in smart agriculture: A survey," Future Generation Computer Systems, vol. 126, pp. 169–184, Jan. 2022. DOI: https://doi.org/10.1016/j.future.2021.08.006
F. Tang, X. Chen, M. Zhao, and N. Kato, "The Roadmap of Communication and Networking in 6G for the Metaverse," IEEE Wireless Communications, vol. 30, no. 4, pp. 72–81, Aug. 2023. DOI: https://doi.org/10.1109/MWC.019.2100721
S. Garg, S. Sinha, A. K. Kar, and M. Mani, "A review of machine learning applications in human resource management," International Journal of Productivity and Performance Management, vol. 71, no. 5, pp. 1590–1610, Feb. 2021. DOI: https://doi.org/10.1108/IJPPM-08-2020-0427
M. I. Ali and A. A. Allawi, "An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18277–18282, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9107
Y. Fouad, N. E. Abdelaziz, and A. M. Elshewey, "IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18929–18934, Dec. 2024. DOI: https://doi.org/10.48084/etasr.9230
J. Nagalapuram and S. Samundeeswari, "A Framework for Smart City Traffic Management utilizing BDA and IoT," Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 18989–18993, Dec. 2024. DOI: https://doi.org/10.48084/etasr.8003
N. Naderializadeh, J. Sydir, M. Simsek, and H. Nikopour, "Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning," in 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications, Atlanta, GA, USA, 2020, pp. 1–5. DOI: https://doi.org/10.1109/SPAWC48557.2020.9154250
Y. Zhang, B. Liu, Y. Gong, J. Huang, J. Xu, and W. Wan, "Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management," in Proceedings of the 5th International Conference on Computer Information and Big Data Applications, Wuhan, China, 2024, pp. 171–175. DOI: https://doi.org/10.1145/3671151.3671183
C. Hardegen, B. Pfülb, S. Rieger, A. Gepperth, and S. Reißmann, "Flow-based Throughput Prediction using Deep Learning and Real-World Network Traffic," in 2019 15th International Conference on Network and Service Management, Halifax, Canada, 2019, pp. 1–9. DOI: https://doi.org/10.23919/CNSM46954.2019.9012716
A. Telikani, A. H. Gandomi, K.-K. R. Choo, and J. Shen, "A Cost-Sensitive Deep Learning-Based Approach for Network Traffic Classification," IEEE Transactions on Network and Service Management, vol. 19, no. 1, pp. 661–670, Mar. 2022. DOI: https://doi.org/10.1109/TNSM.2021.3112283
O. Aouedi, K. Piamrat, and B. Parrein, "Ensemble-Based Deep Learning Model for Network Traffic Classification," IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4124–4135, Dec. 2022. DOI: https://doi.org/10.1109/TNSM.2022.3193748
M. M. Raikar, S. M. Meena, M. M. Mulla, N. S. Shetti, and M. Karanandi, "Data Traffic Classification in Software Defined Networks (SDN) using supervised-learning," Procedia Computer Science, vol. 171, pp. 2750–2759, Jan. 2020. DOI: https://doi.org/10.1016/j.procs.2020.04.299
N. Kumar and A. Ahmad, "Machine learning-based QoS and traffic-aware prediction-assisted dynamic network slicing," International Journal of Communication Networks and Distributed Systems, vol. 28, no. 1, Jan. 2022, Art. no. 27. DOI: https://doi.org/10.1504/IJCNDS.2022.120298
M. P. J. Kuranage, K. Piamrat, and S. Hamma, "Network Traffic Classification Using Machine Learning for Software Defined Networks," in Machine Learning for Networking: Second IFIP TC 6 International Conference, Paris, France, 2019, pp. 28–39. DOI: https://doi.org/10.1007/978-3-030-45778-5_3
M. H. Abidi et al., "Optimal 5G network slicing using machine learning and deep learning concepts," Computer Standards & Interfaces, vol. 76, Jun. 2021, Art. no. 103518. DOI: https://doi.org/10.1016/j.csi.2021.103518
A. A. El-serwy, E. AbdElhalim, and M. A. Mohamed, "Network Slicing Based on Real-Time Traffic Classification in Software Defined Network (SDN) using Machine Learning," Mansoura Engineering Journal, vol. 47, no. 3, Sep. 2022, Art. no. 9. DOI: https://doi.org/10.21608/bfemu.2022.261455
L.-H. Chang, T.-H. Lee, H.-C. Chu, and C.-W. Su, "Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks," Advances in Technology Innovation, vol. 5, no. 4, pp. 216–229, Sep. 2020. DOI: https://doi.org/10.46604/aiti.2020.4286
R. K. Gupta, A. Ranjan, M. A. Moid, and R. Misra, "Deep-Learning Based Mobile-Traffic Forecasting for Resource Utilization in 5G Network Slicing," in Internet of Things and Connected Technologies: Conference Proceedings on 5th International Conference on Internet of Things and Connected Technologies, Patna, India, 2020, pp. 410–424. DOI: https://doi.org/10.1007/978-3-030-76736-5_38
O. Aouedi, K. Piamrat, S. Hamma, and J. K. M. Perera, "Network traffic analysis using machine learning: an unsupervised approach to understand and slice your network," Annals of Telecommunications, vol. 77, no. 5, pp. 297–309, Jun. 2022. DOI: https://doi.org/10.1007/s12243-021-00889-1
A. E.-S. Saqr, A. M. Elshewey, S. K. Raju, and M. M. Eid, "A Comprehensive Review on Optimizing Machine Learning Models for Early Detection and Forecasting of Monkeypox Outbreaks," Journal of Artificial Intelligence and Metaheuristics, vol. 8, no. 1, pp. 09–20, Aug. 2024. DOI: https://doi.org/10.54216/JAIM.080102
A. E.-S. Saqr and A. M. Elshewey, "Utilizing Machine Learning for Predicting Lyme disease Trends and Enhancing Diagnostic Accuracy," Metaheuristic Optimization Review, vol. 1, no. 2, pp. 48–58, Dec. 2024. DOI: https://doi.org/10.54216/MOR.010205
E.-S. M. El-kenawy, N. Khodadadi, S. Mirjalili, A. A. Abdelhamid, M. M. Eid, and A. Ibrahim, "Greylag Goose Optimization: Nature-inspired optimization algorithm," Expert Systems with Applications, vol. 238, no. E, Mar. 2024, Art. no. 122147. DOI: https://doi.org/10.1016/j.eswa.2023.122147
"IP Network Traffic Flows Labeled with 75 Apps." Kaggle. [Online]. Available: https://www.kaggle.com/datasets/jsrojas/ip-network-traffic-flows-labeled-with-87-apps.
"Intrusion detection evaluation dataset (CIC-IDS2017)." University of New Brunswick. [Online]. Available: https://www.unb.ca/cic/datasets/ids-2017.html.
Downloads
How to Cite
License
Copyright (c) 2025 Rasool Altaee, Ghaidaa A. Al-Sultany
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
