The DOI matches its Crossref record; 52 of the 56 references it cites were found in a registry or an index.
Robust Network Traffic Classification
Open accessAbstract
As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.
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“Therefore, even with small labeled training datasets, RTC can achieve a higher flow and byte accuracy of up to 15% and 10%, respectively, in comparison to the second best technique.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [497]
In the citing paper
“Consequentially, joint application of supervised and unsupervised ML for traffic classification [137, 497] has demonstrated success.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [497]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
(2006) · Springer eBooks · Cited by 8,123
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Kittler, J., Hatef, M., Duin, R. P. W., & Matas, J. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.667881 · Cited by 5,394
Information Theory, Inference, and Learning Algorithms
(2004) · Kybernetes · doi:10.1108/03684920410534506 · Cited by 4,937
Pattern Recognition and Machine Learning
Neal, R. M. (2007) · Technometrics · doi:10.1198/tech.2007.s518 · Cited by 4,629
(2000) · Applied Physics Letters · doi:10.1162/153244303322753616 · Cited by 3,581
A survey of techniques for internet traffic classification using machine learning
Nguyen, T. T. T., & Armitage, G. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080406 · Cited by 1,664
Research works printed but not linked: 2
- 2008, ?Cisco WAN and application optimization solution guide ?
- erman, 2006, Internet traffic identification using machine learning, Proc IEEE Global Telecommun Conf, 1
Cited by
Sorted by citations
Items 201–300 of 390
Noferesti, M., & Jalili, R. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.106943 · Cited by 9
Research and Improvement of Encrypted Traffic Classification Based on Convolutional Neural Network
Zhou, Y., & Cui, J. (2020) · International Conference on Computer Science and Network Technology (ICCSNT) · doi:10.1109/iccsnt50940.2020.9305018 · Cited by 9
Towards Open World Traffic Classification
Liu, Z., Cai, L., Zhao, L., Yu, A., & Meng, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86890-1_19 · Cited by 9
A novel semi-supervised learning method for Internet application identification
Chen, Z., Liu, Z., Peng, L., Wang, L., & Zhang, L. (2015) · Soft Computing · doi:10.1007/s00500-015-1892-1 · Cited by 9
Zeng, Y., Qi, Z., Chen, W., Huang, Y., Zheng, X., & Qiu, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10271 · Cited by 10
Enhancing Network Traffic Classification with Large Language Models
Zhou, H., Huang, X., & Deng, L. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825308 · Cited by 8
P2P traffic classification using clustering technology
Tseng, C.-M., Guo-Tai, H., & Liu, T.-J. (2016) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2016.7843994 · Cited by 8
Multimedia Traffic Classification for Imbalanced Environment
Wu, Z., Dong, Y., Jin, J., Wei, H., & Xie, G. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3153925 · Cited by 8
Multi-Granularity Abnormal Traffic Detection Based on Multi-Instance Learning
Jiang, X., Zhang, H., & Zhou, Y. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3322152 · Cited by 8
Alcock, S., Möller, J.-P., & Nelson, R. (2016) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2987443.2987447 · Cited by 8
Frame-by-frame Wi-Fi attack detection algorithm with scalable and modular machine-learning design
Rizzi, A., Granato, G., & Baiocchi, A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106188 · Cited by 8
A Survey of Privacy Protection and Network Security in User On-Demand Anonymous Communication
He, Y., Zhang, M., Yang, X., Luo, J., & Chen, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2981517 · Cited by 8
NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
Iglesias, F., Ferreira, D. C., Vormayr, G., Bachl, M., & Zseby, T. (2020) · Applied Sciences · doi:10.3390/app10124307 · Cited by 8
MAGNet: Machine Learning Guided Application-Aware Networking for Data Centers
Chang, H., Kodialam, M. S., Lakshman, T. V., Mukherjee, S., der Merwe, J. V., & Zaheer, Z. (2021) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2021.3087447 · Cited by 8
Machine Learning to Automate Network Segregation for Enhanced Security in Industry 4.0
Saghezchi, F. B., Μαντάς, Γ., Ribeiro, J., Esfahani, A., Alizadeh, H., Bastos, J., & Rodrı́guez, J. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-05195-2_15 · Cited by 8
Machine Learning in Networking
Robertazzi, T. G., & Li, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-36704-6_7 · Cited by 9
Towards targeted and universal adversarial attacks against network traffic classification
Ding, R., Sun, L., Zhiyi, D., Zang, W., & Dai, L. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104470 · Cited by 7
Deep neural network-based automatic unknown protocol classification system using histogram feature
Jung, Y.-G., & Jeong, C.-M. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03108-w · Cited by 7
Machine Learning and Data Networks: Perspectives, Feasibility, and Opportunities
Lozada-Yánez, R., Molina-Granja, F., Yánez, P. E. L., & Yungán, J. I. G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-45691-7_26 · Cited by 7
Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic
Hagos, D. H., Engelstad, P. E., Yazidi, A., & Kure, Ø. (2018) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2018.8487396 · Cited by 7
Tanhatalab, M. R., Yousefi, H. H. N., Hosseini, H. M., Bonab, M. M., Fakharian, V., & Abarghouei, H. (2020) · IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI) · doi:10.1109/sami48414.2020.9108742 · Cited by 7
Jin, D., Xie, J., Chen, S., Yang, J., Liu, X., & Wang, W. (2020) · Cited by 7
Grano-GT: A granular ground truth collection tool for encrypted browser-based Internet traffic
Zaki, F., Gani, A., Tahaei, H., Furnell, S., & Anuar, N. B. (2020) · Computer Networks · doi:10.1016/j.comnet.2020.107617 · Cited by 7
Jing, X., Zhao, J., Yan, Z., Pedrycz, W., & Li, X. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.12.017 · Cited by 7
Clustering unknown network traffic with dual-path autoencoder
Fu, Y., Li, X., Li, X., Zhao, S., & Wang, F. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-08138-9 · Cited by 7
Traffic Classification Based on Graph Convolutional Network
Ji, X., & Meng, Q. (2020) · IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca49918.2020.9213630 · Cited by 8
A novel method for network traffic classification based on robust support vector machine
Qu, H., Jiang, J., Zhao, J., Zhang, Y., & Yang, J. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4092 · Cited by 7
Practical and configurable network traffic classification using probabilistic machine learning
Chen, J., Breen, J., Phillips, J. M., & der Merwe, J. V. (2021) · Cluster Computing · doi:10.1007/s10586-021-03393-2 · Cited by 7
Pushing intelligence to the network edge
Salman, O., Chaddad, L., Elhajj, I. H., Chehab, A., & Kayssi, A. I. (2018) · International Conference on Software Defined Systems (SDS) · doi:10.1109/sds.2018.8370427 · Cited by 7
Identification of adaptive video streams based on traffic correlation
Biernacki, A. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7183-6 · Cited by 7
Fine grained classification of Internet video traffics
Dong, Y., Yao, L.-T., & Hai-xian, S. (2015) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc.2015.7412578 · Cited by 6
Robust Network Flow Classification against Malicious Feature Manipulation
Li, Y., Liang, B., & Tizghadam, A. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148644 · Cited by 6
Kumar, S., Suresh, A., Anand, S. R., Chokkanathan, K., & Vijayasarathy, M. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08208-6 · Cited by 6
Augmenting DiffServ operations with dynamically learned classes of services
Aureli, D., Cianfrani, A., Listanti, M., Polverini, M., & Secci, S. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108624 · Cited by 6
Encrypted Live Streaming Channel Identification With Time-Sync Comments
Wang, M., Tang, X., Chen, F., & Lu, Q. (2022) · IEEE Access · doi:10.1109/access.2022.3157716 · Cited by 6
Malware Network Traffic Classification on the Edge
Chen, E., & Perez-Pons, A. (2022) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass56207.2022.00118 · Cited by 6
Common Knowledge Based Transfer Learning for Traffic Classification
Xiao, Y., Sun, H., Zhuang, Z., Wang, J., & Qi, Q. (2018) · Conference on Local Computer Networks · doi:10.1109/lcn.2018.8638070 · Cited by 6
LightSEEN: Real-Time Unknown Traffic Discovery via Lightweight Siamese Networks
Ji, L., Gu, C., Wei, F., Zhang, X., Hu, X., Guo, J., & Liu, W. (2021) · Security and Communication Networks · doi:10.1155/2021/8267298 · Cited by 6
An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection
Xu, H., Hu, Y., Cao, W., & Han, L. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.039227 · Cited by 6
A survey of classification algorithms for network traffic
Deebalakshmi, R., & Jyothi, V. L. (2016) · doi:10.1109/iconstem.2016.7560941 · Cited by 6
Multimedia Content Delivery in OpenFlow SDN: An Approach Based on a Multimedia Gateway
Diório, R. F., & Timóteo, V. S. (2016) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2016.0121 · Cited by 6
Machine learning–based haptic‐enabled surgical navigation with security awareness
Tai, Y., Wei, L., Zhou, H., Li, Q., Huang, X., Shi, J., & Nahavandi, S. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4908 · Cited by 6
Intelligent Traffic Classification for Detecting DDoS Attacks using SDN/OpenFlow
Bakker, J. N. (2017) · doi:10.26686/wgtn.17064107.v1 · Cited by 6
Li, H., Kang, H., Liu, C., Wang, R., Li, J., Sun, G., Wang, J., Liang, S., & Mao, S. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3649259 · Cited by 7
Traffic Identification in Big Internet Data
Wang, B., Zhang, J., Zhang, Z., Luo, W., & Xia, D. (2016) · Springer eBooks · doi:10.1007/978-3-319-27763-9_3 · Cited by 5
Self-citationInline high‐bandwidth network analysis using a robust stream clustering algorithm
Noferesti, M., & Jalili, R. (2019) · IET Information Security · doi:10.1049/iet-ifs.2018.5287 · Cited by 5
Traffic classification for connectionless services with incremental learning
Punitha, V., & Mala, C. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.11.017 · Cited by 5
Zhang, M., Sun, W., Tian, J., Zheng, X., & Guan, S. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.860 · Cited by 5
A Novel Approach for Semi-Supervised Network Traffic Classification
Huo, Y., Song, C., Zhou, M., Lv, R., & Yang, Y. (2022) · International Conference on Advanced Infocomm Technology · doi:10.1109/icait56197.2022.9862675 · Cited by 6
Relative Frequency-Rank Encoding for Unsupervised Network Anomaly Detection
Kim, M., Jang, W., Hur, J., & Yoon, M. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3391396 · Cited by 5
Artificial Intelligence in Cryptographic Evolution
Ishtaiwi, A. M., Khaldy, M. A. A., Al–Qerem, A., Aldweesh, A. Y., & Almomani, A. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5330-1.ch002 · Cited by 5
Combining Renyi Entropy and EWMA to Detect Common Attacks in Network
Yan, R. (2016) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800141650021x · Cited by 5
GMAF: A Novel Gradient-Based Model with ArcFace for Network Traffic Classification
Xia, Y., Xiong, G., Li, Z., Gou, G., & Liu, C. (2021) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys53884.2021.00064 · Cited by 5
Imbalanced Internet Traffic Classification Using Ensemble Framework
Oeung, P., & Shen, F. (2019) · International Conference on Information Networking · doi:10.1109/icoin.2019.8717977 · Cited by 5
Applying Softmax Classifiers to Open Set
Webb, D. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-1699-3_9 · Cited by 5
Gu, X., Ni, T., Fan, Y., & Wang, W. (2020) · Annals of Telecommunications · doi:10.1007/s12243-020-00767-2 · Cited by 6
Yu, H., Zhao, Y. T., Xiong, G., Guo, L., Li, Z., & Wang, Y. (2014) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2660267.2662391 · Cited by 5
BitCoding: Protocol Type Agnostic Robust Bit Level Signatures for Traffic Classification
Hubballi, N., & Swarnkar, M. (2017) · Globecom · doi:10.1109/glocom.2017.8254001 · Cited by 5
Are Network Attacks Outliers? A Study of Space Representations and Unsupervised Algorithms
Iglesias, F., Hartl, A., Zseby, T., & Zimek, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43887-6_13 · Cited by 5
Yang, L., Finamore, A., Jun, F., & Rossi, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03182 · Cited by 5
A multi-sensor deep metric learning method for unknown anomaly detection in unmanned aerial vehicles
Zhao, J., & Xu, X. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111584 · Cited by 4
Multiview Correlation-Aware Network Traffic Detection on Flow Hypergraph
Zhou, J., Fu, W., Song, H., Yu, S., & Xuan, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3627807 · Cited by 5
Traffic classification in server farm using supervised learning techniques
Punitha, V., & Mala, C. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05030-2 · Cited by 4
Attention to Traffic: Network Traffic Classification using Attention-Based CNNs
Anwar, M. A., Agrawal, M., Saroha, N., & Bhat, A. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306767 · Cited by 4
Aziz, W. A., Qureshi, H. K., Iqbal, A., Al‐Dulaimi, A., & Al–Rubaye, S. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437078 · Cited by 4
A Zero-Shot Learning-Based Detection Model Against Zero-Day Attacks in IoT
Gao, X., Chen, K., Zhao, Y., Zhang, P., Han, L., & Zhang, D. (2024) · International Conference on Electronic Technology and Information Science (ICETIS) · doi:10.1109/icetis61828.2024.10593684 · Cited by 4
A SMA-SVM-Based Prediction Model for the Tailings Discharge Volume After Tailings Dam Failure
Liu, G., Zhao, B., Kong, X., Xin, Y., Wang, M., & Zhang, Y. (2025) · Water · doi:10.3390/w17040604 · Cited by 4
Wang, Z., Lin, W., Chen, Y., & Vai, M. I. (2025) · IEEE Access · doi:10.1109/access.2025.3545476 · Cited by 4
Improved application of transfer learning in network traffic classification
Shang, F., Li, S., & He, J. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1682/1/012011 · Cited by 4
Correlation Between GitHub Stars and Code Vulnerabilities
Naveed, M. S. (2022) · Journal of Computing & Biomedical Informatics · doi:10.56979/401/2022/111 · Cited by 4
Torres, L. M., Magaña, E., Morató, D., García-Jiménez, S., & Izal, M. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.10.007 · Cited by 4
Applications and use Cases of Multilevel Granularity for Network Traffic Classification
Zaki, F., Gani, A., & Anuar, N. B. (2020) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa48992.2020.9068697 · Cited by 4
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A SurveyPreprint
Zhang, X., Chen, C., Xie, Y., Chen, X., Zhang, J., & Xiang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06300 · Cited by 4
Self-citationYang, Y., Fan, C., Chen, S., Gao, Z., & Rui, L. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103676 · Cited by 3
An encrypted traffic classification method based on autoencoders and convolutional neural networks
Xu, S., Han, J., Wang, J., & Bai, Y. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333276 · Cited by 5
Distributed Detection of Zero-Day Network Traffic Flows
Miao, Y., Pan, L., Rajasegarar, S., Zhang, J., Leckie, C. A., & Xiang, Y. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-0292-3_11 · Cited by 3
Self-citationAutonomous QoS-Based Mechanism for Resource Allocation in LTE-Advanced Pro Networks
Santos, E. C. (2018) · IEEE Colombian Conference on Communications and Computing (COLCOM) · doi:10.1109/colcomcon.2018.8466714 · Cited by 3
Traffic Pattern Plot: Video Identification in Encrypted Network Traffic
Kamal, A., Bukhari, S. M. A. H., Khan, M. U. S., Maqsood, T., & Fayyaz, M. A. B. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7663-6_8 · Cited by 3
Machine Learning Empowered Intelligent Data Center Networking
Wang, T., Li, B., Chen, M., & Yu, S. (2022) · SpringerBriefs in computer science · doi:10.1007/978-981-19-7395-6_3 · Cited by 3
Activity Detection from Encrypted Remote Desktop Protocol Traffic
Lapczyk, L., & Skillicorn, D. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32636-3_14 · Cited by 3
Network traffic classification in the visual communication of music based on support vector machine
Zheng, Y. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-01998-x · Cited by 3
Liang, Y.-L., Wang, F., Chen, S.-H., Chen, B., & Bo, Y. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.02.010 · Cited by 6
Sangeetha, R., Kuriakose, B. M., Naveen., E., V, Jenefa, A., & Lincy, A. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-231113 · Cited by 3
RetractedI know I don’t know: an evidential deep learning framework for traffic classification
Li, S., Luo, L., Zhou, Y., Guo, D., & Xu, X. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-3922-6 · Cited by 3
Network Traffic Classification by Program Synthesis
Shi, L., Li, Y., Loo, B. T., & Rajeev Alur, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72016-2_23 · Cited by 3
Mohammadi, R., Akleylek, S., Ghaffari, A., & Shirmarz, A. (2022) · Cluster Computing · doi:10.1007/s10586-022-03729-6 · Cited by 3
Fernandes, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-54521-9_4 · Cited by 3
Midiag: A Sequential Trace-Based Fault Diagnosis Framework for Microservices
Meng, L., Sun, Y., & Zhang, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59592-0_9 · Cited by 3
Anomaly Detection of actual IoT traffic flows through Deep Learning
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021) · International Conference on Machine Learning and Applications · doi:10.1109/icmla52953.2021.00275 · Cited by 3
DACS: A Double-layer Application Classification Scheme for Hybrid Zero-day Traffic
Liang, Y., Wang, F., & Chen, S. (2022) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct56141.2022.10073219 · Cited by 3
Low-Latency Internet Traffic Identification using Machine Learning with Trend-based Features
Enisoglu, R., & Rakočević, V. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10183084 · Cited by 3
Yao, H., Jiang, C., & Qian, Y. (2019) · Wireless networks · doi:10.1007/978-3-030-15028-0_3 · Cited by 3
Deep-Learning-Based Uncertainty-Estimation Approach for Unknown Traffic Identification
Le, S., Lai, Y., Wang, Y., & He, H. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3437332 · Cited by 2
Neha, N., & Bhatia, T. (2025) · International Conference on Computer Technologies (ICCTech) · doi:10.1109/icctech66294.2025.00028 · Cited by 2
FlowXpert: Context-Aware Flow Embedding for Enhanced Traffic Detection in IoT Network
Zha, C., Pan, H., Bai, B., Wu, J., & Zhang, R. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3651574 · Cited by 2
Self-citationEnisoglu, R., & Inan, B. A. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112578 · Cited by 3
Continuously running genetic algorithm for real-time networking device optimization
Mandelbaum, A., Haritan, D., & Shechtman, N. (2021) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3449639.3459272 · Cited by 2
In-Network Flow Classification With Knowledge Distillation
Lin, K. C., & Li, C. (2021) · IEEE Access · doi:10.1109/access.2021.3100057 · Cited by 2
An Efficient Network Classification based on Various-Widths Clustering and Semi-supervised Stacking
Almalawi, A., & Fahad, A. (2021) · IEEE Access · doi:10.1109/access.2021.3123451 · Cited by 3
Mobile traffic classification through burst traffic statistical features
Anamuro, C. V., & Lagrange, X. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-spring57618.2023.10200032 · Cited by 2
Metadata
- Authors
- Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J.
- Affiliations
- Deakin University (AU)
- Temple University (US)
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 2015-08
- Volume
- 23
- Issue
- 4
- Pages
- 1257-1270
- ISSN
- 1063-6692 (ISSN-L)
- 1558-2566
- OpenAlex
- W2161251616
- Cited by
- 391
- References
- 56
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 22 · 2023: 34 · 2022: 38 · 2021: 48 · 2020: 58 · 2019: 54 · 2018: 40 · 2017: 30 · 2016: 18 · 2015: 7
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- IEEE/ACM Transactions on Networking (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- network traffic classification, hybrid supervised and unsupervised learning, application identification, parameter optimization, network security, network management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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