Jul 2026· De Computis· Vol 15, pp. 445· 0 citations· 13 references
Computer Science
TL;DR
Although the experiments were conducted using a pre-5G mobile traffic dataset, the results demonstrate that supervised machine learning can effectively classify encrypted mobile application traffic and provide a practical foundation for application-aware QoS policy enforcement in future 5G and next-generation mobile networks.
Abstract
The increasing complexity and volume of mobile network traffic present significant challenges to maintain consistent Quality of Service (QoS) across diverse applications. Accurate traffic classification enables application-aware resource allocation by distinguishing applications with different bandwidth, latency, and reliability requirements. Traditional classification techniques, including port-based identification and Deep Packet Inspection (DPI) have become inadequate and less effective due to widespread encryption, port masquerading, and growing privacy concerns. This paper presents a supervised learning-based approach for application-level network traffic classification specifically as a foundation for QoS optimization in future 5G networks. Since publicly available labeled 5G traffic datasets remain limited, this study uses the MIRAGE-2019 mobile traffic dataset as a proxy dataset to evaluate the proposed classification framework. A Random Forest classifier was implemented using flow-level statistical features extracted from the mobile application traffic. The framework further incorporates a rule-based QoS policy mapping informed by RFC 4594 DiffServ service class guidelines to assign application-specific priority levels, bandwidth requirements, latency sensitivity, and jitter tolerance. Experimental evaluation achieved an overall classification Accuracy of 71.83%, a Macro F1-score of 0.6701, and a Weighted F1-score of 0.7227 across twenty mobile applications. Although the experiments were conducted using a pre-5G mobile traffic dataset, the results demonstrate that supervised machine learning can effectively classify encrypted mobile application traffic and provide a practical foundation for application-aware QoS policy enforcement in future 5G and next-generation mobile networks.
Effective NTC plays a vital role in enhancing bandwidth efficiency, ensuring network security, and maintaining Quality of Service (QoS) in todays communication systems. Conventional methods like port-based as well as payload-based classification are no more reliable because of the rise of encoded traffic and dynamic applications. As an alternative, Machine Learning (ML) approaches can automatically identify patterns in the statistical characteristics of network flows, offering more flexibility and accuracy. This paper presents a survey of Machine Learning that was used in NTC. The main focus on latest advances such as Protocol Detection, Traffic Behavior Analysis and Application Identification.Resent Trends, Evaluation Metrices, Benchmark Datasets and Machine Learning Algorithms are the core things that was discussed in this review. Along with Class Imbalance, Concept drift and encrypted traffic, these kinds of problem were also discussed in it. Beside these things, to understand the ML algorithms and make baseline benchmark as KDD dataset to represent comparative analysis.
Maninder Singh Zandu, Sandeep Kad· International Journal of Adv...· 0 citations
Network traffic classification has become essential for managing, securing, and optimizing next-generation networks (NGNs) that support IoT, cloud computing, SDN, NFV, and 5G technologies. Traditional classification methods based on port numbers and deep packet inspection are increasingly ineffective due to encrypted traffic, dynamic port allocation, and evolving application behaviors.This study presents a deep learning-based approach for intelligent network traffic classification. It reviews the evolution of traffic classification techniques and proposes a hybrid CNN-LSTM model that combines packet-level feature extraction with temporal sequence learning. The framework effectively captures spatial and temporal characteristics of network traffic, enabling accurate classification of both encrypted and non-encrypted flows. Experimental evaluation using benchmark datasets demonstrates that the proposed model outperforms conventional machine learning methods, achieving classification accuracy above 97%. Performance is validated using metrics such as accuracy, precision, recall, F1-score, and classification efficiency. The results highlight the potential of deep learning to support real-time traffic analysis, network security, QoS management, and resource optimization in modern communication networks. The proposed framework provides an intelligent and scalable solution for traffic classification in future autonomous and secure networking environments.
Chen Wei, Hiroshi Tanaka· International Journal of App...· 0 citations
Encrypted network communications reduce the effectiveness of payload based traffic identification and complicate the translation of traffic analysis into network access control decisions. This study evaluates a payload independent workflow that connects flow based multiclass classification with administrator triggered, time limited firewall enforcement. The experiment used the public ISCXVPN2016 benchmark. After removing 18,719 duplicate records, 40,987 unique flows remained; all 23 available numerical flow features were retained without feature selection or normalization. A Random Forest classifier with 300 trees was selected using five-fold cross-validated grid search on a stratified 80% training partition and evaluated on an independent 8,198 sample test set covering 14 VPN and non-VPN traffic classes. The model achieved 88.01% accuracy, 88.00% weighted precision, 88.01% weighted recall, and 87.97% weighted F1-score. It exceeded the strongest reproduced baseline, K-Nearest Neighbors, by 16.09 percentage points in accuracy and 16.29 percentage points in weighted F1-score. Supplemental five-fold evaluation produced a mean accuracy of 88.05% with a 0.36 percentage point standard deviation. The trained classifier was integrated with a web application in which administrators review predicted flows and initiate temporary MikroTik RouterOS rules. All 30 temporary blocking entries observed in the evaluation database reached the Unblocked state with recorded timestamps, demonstrating rule lifecycle traceability at the database level. The findings show that Random Forest can provide competitive flow based classification while supporting an auditable, human controlled access control workflow; however, device level reliability and cross dataset generalizability require further validation.
Wahyu Isnan, Yulia Fatmi, Resmi Darni et al.· Intechno Journal (Informatio...· 0 citations
A slice-aware deep learning framework for the joint prediction of traffic demand and multiple KPIs within a simulation-driven environment and shows that deep learning models more effectively capture nonlinear slice-level dynamics compared with traditional forecasting approaches.
Sultan Ertas, B. Cavusoglu· IEEE Access· 0 citations
There is a need for more research on network traffic classification as it is useful in cybersecurity, intrusion detection, and network management. However, on a real-world level, datasets that record network traffic contain high bias as they reflect a disproportionate number of traffic attacks and a surplus of routine traffic. This research presents a modified network traffic classification framework to tackle class imbalance in multi-network traffic classification. The framework combines in one pipeline data preprocessing, adaptive imbalance correction, and machine learning classification. This was validated on a Wireless Sensor Network dataset with five classes of traffic. Random Forest and Neural Network models were compared for imbalanced and balanced conditions. The proposed hybrid framework shows significant class imbalance improvement and detection of minor classes. The balanced Random Forest classifier, from the baseline implementation, had about 97.43% of correctly identified attacks with a significant increase in the classification metrics, i.e. precision, recall, F1-score. Analysis of the confusion matrix and the ROC curve indicated that the number of falsely identified attacks (< class) is low and that class identification separability improved. The results showed that in the network traffic environment, awareness of class imbalance learning is effective; furthermore, the hybrid framework shows consistent and flexible results.
Varinder Kaur, Amandeep Kaur Virk· International journal of com...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices and their integration into increasingly interconnected applications have substantially expanded the attack surface of modern networked systems. The heterogeneous nature and high volume of IoT traffic make timely and reliable identification of malicious activities increasingly important for maintaining the security and resilience of IoT-enabled environments. This study investigates the effectiveness of maching learning approaches for supervised malicious traffic classification in IoT networks using the ACI-IoT-2023 dataset. A comparative experimental study is conducted across binary and eleven-class classification tasks to examine the capability of different learning approaches to distinguish benign and malicious traffic and identify diverse attack categories. The results demonstrate strong classification performance across the evaluated approaches, with XGBoost achieving the highest ROC-AUC in binary classification and the Decision Tree delivering the best overall performance in eleven-class classification. Further analysis of feature importance identifies several flow-level features that contribute substantially to classification performance. Overall, the findings demonstrate the effectiveness of machine learning-based approaches for accurate and efficient malicious traffic classification in IoT networks.
Connor Gladish, Molly Corgan, J. Moss et al.· Electronics· 0 citations
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