Issues and future directions in traffic classification

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

BLINC

Karagiannis, T., Papagiannaki, K., & Faloutsos, M. (2005) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/1080091.1080119 · Cited by 997

Toward the Accurate Identification of Network Applications

Moore, A. W., & Papagiannaki, K. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-31966-5_4 · Cited by 801

Transport layer identification of P2P traffic

Karagiannis, T., Broido, A., Faloutsos, M., & claffy, K. (2004) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1028788.1028804 · Cited by 708

A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

Williams, N., Zander, S., & Armitage, G. J. (2006) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1163593.1163596 · Cited by 698

Contention avoidance and resolution schemes in bufferless all-optical packet-switched networks: a survey

Rahbar, A. G., & Yang, O. W. W. (2008) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2008.080408 · Cited by 676

Internet traffic classification demystified: myths, caveats, and the best practices

Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., & Lee, K. (2008) · Proceedings of the 2008 ACM CoNEXT Conference on - CONEXT '08 · doi:10.1145/1544012.1544023 · Cited by 485

A Survey on Internet Traffic Identification

Callado, A. C., Kamienski, C., Szabó, G., Gero, B. P., Kelner, J., Fernandes, S., & Sadok, D. (2009) · IEEE Communications Surveys and Tutorials · doi:10.1109/surv.2009.090304 · Cited by 421

Early application identification

Bernaille, L., Teixeira, R., & Salamatian, K. (2006) · Proceedings of the 2006 ACM CoNEXT conference on - CoNEXT '06 · doi:10.1145/1368436.1368445 · Cited by 374

Efficient application identification and the temporal and spatial stability of classification schema

Li, W., Canini, M., Moore, A. W., & Bolla, R. (2008) · Computer Networks · doi:10.1016/j.comnet.2008.11.016 · Cited by 158

Deep packet: a novel approach for encrypted traffic classification using deep learning

Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190

A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities

Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036

End-to-end encrypted traffic classification with one-dimensional convolution neural networks

Wang, W., Zhu, M., Wang, J., Zeng, X., & Yang, Z. (2017) · 2017 IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2017.8004872 · Cited by 991

Malware traffic classification using convolutional neural network for representation learning

Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017) · 2017 International Conference on Information Networking (ICOIN) · doi:10.1109/icoin.2017.7899588 · Cited by 1,177

Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2019) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2019.2899085 · Cited by 525

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Abbasi, M., Shahraki, A., & Taherkordi, A. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.01.021 · Cited by 484

A survey of methods for encrypted traffic classification and analysis

Velan, P., Čermák, M., Čeleda, P., & Drašar, M. (2015) · International Journal of Network Management · doi:10.1002/nem.1901 · Cited by 400

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

Pacheco, F., Expósito, E., Gineste, M., Baudoin, C., & Aguilar, J. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2883147 · Cited by 387

A Survey of Payload-Based Traffic Classification Approaches

Finsterbusch, M., Richter, C., Rocha, E., Müller, J.-A., & Hänßgen, K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.100613.00161 · Cited by 337

MIMETIC: Mobile encrypted traffic classification using multimodal deep learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.106944 · Cited by 291

A Method of Few-Shot Network Intrusion Detection Based on Meta-Learning Framework

Xu, C., Shen, J., & Du, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2991876 · Cited by 289

The rise of traffic classification in IoT networks: A survey

Tahaei, H., Afifi, F., Asemi, A., Zaki, F., & Anuar, N. B. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102538 · Cited by 278

Independent comparison of popular DPI tools for traffic classification

Bujlow, T., Carela-Español, V., & Barlet‐Ros, P. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.11.001 · Cited by 245

FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification

Shapira, T., & Shavitt, Y. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3071441 · Cited by 235

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

When Machine Learning Meets Privacy in 6G: A Survey

Sun, Y., Liu, J., Wang, J., Cao, Y., & Kato, N. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3011561 · Cited by 211

Classification of Encrypted Traffic With Second-Order Markov Chains and Application Attribute Bigrams

Shen, M., Wei, M., Zhu, L., & Wang, M. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2692682 · Cited by 200

FlowPic: Encrypted Internet Traffic Classification is as Easy as Image Recognition

Shapira, T., & Shavitt, Y. (2019) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2019.8845315 · Cited by 212

Do Switches Dream of Machine Learning?

Xiong, Z., & Zilberman, N. (2019) · ACM Workshop on Hot Topics in Networks (HotNets) · doi:10.1145/3365609.3365864 · Cited by 198

DISTILLER: Encrypted traffic classification via multimodal multitask deep learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.102985 · Cited by 185

Multi-classification approaches for classifying mobile app traffic

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2017) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2017.11.007 · Cited by 184

Multi class SVM algorithm with active learning for network traffic classification

Dong, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114885 · Cited by 175

Mobile Encrypted Traffic Classification Using Deep Learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2018) · Network Traffic Measurement and Analysis Conference (TMA) · doi:10.23919/tma.2018.8506558 · Cited by 155

Encrypted Traffic Classification with a Convolutional Long Short-Term Memory Neural Network

Zou, Z., Ge, J., Zheng, H., Wu, Y., Han, C., & Yao, Z. (2018) · 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/smartcity/dss.2018.00074 · Cited by 150

A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning

Wang, P., Chen, X., Ye, F., & Sun, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2912896 · Cited by 148

Toward effective mobile encrypted traffic classification through deep learning

Aceto, G., Ciuonzo, D., Montieri, A., & Pescapè, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.036 · Cited by 147

On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification

Lahsen-Cherif, I., & Kortebi, A. (2019) · IFIP Wireless Days · doi:10.1109/wd.2019.8734193 · Cited by 143

Flow-Based Encrypted Network Traffic Classification With Graph Neural Networks

Huoh, T.-L., Luo, Y., Li, P., & Zhang, T. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3227500 · Cited by 149

Tree-RNN: Tree structural recurrent neural network for network traffic classification

Ren, X., Gu, H., & Wei, W. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114363 · Cited by 137

A review on machine learning–based approaches for Internet traffic classification

Salman, O., Elhajj, I. H., Kayssi, A., & Chehab, A. (2020) · Annals of Telecommunications · doi:10.1007/s12243-020-00770-7 · Cited by 136

NFStream

Aouini, Z., & Pekár, A. (2022) · Computer Networks · doi:10.1016/j.comnet.2021.108719 · Cited by 122

Protocol misidentification made easy with format-transforming encryption

Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2013) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2508859.2516657 · Cited by 120

PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN

Wang, P., Li, S., Ye, F., Wang, Z., & Zhang, M. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148946 · Cited by 118

Anonymity Services Tor, I2P, JonDonym: Classifying in the Dark (Web)

Montieri, A., Ciuonzo, D., Aceto, G., & Pescapè, A. (2018) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2018.2804394 · Cited by 116

PBCNN: Packet Bytes-based Convolutional Neural Network for Network Intrusion Detection

Yu, L., Dong, J., Chen, L., Li, M., Xu, B., Li, Z., Qiao, L., Liu, L., Zhao, B., & Zhang, C. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108117 · Cited by 113

QoS Provisioning in Converged Satellite and Terrestrial Networks: A Survey of the State-of-the-Art

Niephaus, C., Kretschmer, M., & Ghinea, G. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2561078 · Cited by 113

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

Wang, X., Chen, S., & Su, J. (2020) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps50562.2020.9162891 · Cited by 113

A Survey of Automatic Protocol Reverse Engineering Tools

Narayan, J., Shukla, S. K., & Clancy, T. C. (2015) · ACM Computing Surveys · doi:10.1145/2840724 · Cited by 104

Reviewing Traffic Classification

Valenti, S., Rossi, D., Dainotti, A., Pescapè, A., Finamore, A., & Mellia, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36784-7_6 · Cited by 103

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

I Know What You Saw Last Minute—Encrypted HTTP Adaptive Video Streaming Title Classification

Dubin, R., Dvir, A. Z., Pele, O., & Hadar, O. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2730819 · Cited by 101

Internet Traffic Classification Based on Incremental Support Vector Machines

Sun, G., Chen, T., Su, Y., & Li, C. (2018) · Mobile Networks and Applications · doi:10.1007/s11036-018-0999-x · Cited by 96

A Survey on Encrypted Traffic Classification

Cao, Z., Xiong, G., Zhao, Y. T., Li, Z., & Guo, L. (2014) · Communications in computer and information science · doi:10.1007/978-3-662-45670-5_8 · Cited by 96

HomeSnitch

OConnor, T., Mohamed, R. E., Miettinen, M., Enck, W., Reaves, B., & Sadeghi, A. (2019) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3317549.3323409 · Cited by 95

Network traffic classification based on transfer learning

Sun, G., Liang, L., Chen, T., Xiao, F., & Lang, F. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.03.005 · Cited by 95

MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation

Aceto, G., Ciuonzo, D., Montieri, A., Persico, V., & Pescapè, A. (2019) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/cccs.2019.8888137 · Cited by 93

MS-LSTM: A multi-scale LSTM model for BGP anomaly detection

Cheng, M., Xu, Q., Jianming, L. V., Liu, W., Li, Q., & Wang, J. (2016) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2016.7785326 · Cited by 93

Encrypted Network Traffic Classification Using Deep and Parallel Network-in-Network Models

Bu, Z., Zhou, B., Cheng, P., Zhang, K., & Ling, Z.-H. (2020) · IEEE Access · doi:10.1109/access.2020.3010637 · Cited by 89

Self-attentive deep learning method for online traffic classification and its interpretability

Xie, G., Li, Q., & Jiang, Y. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108267 · Cited by 93

Toward an efficient and scalable feature selection approach for internet traffic classification

Fahad, A., Tari, Z., Khalil, I., Habib, I., & Alnuweiri, H. (2013) · Computer Networks · doi:10.1016/j.comnet.2013.04.005 · Cited by 89

In-Network Machine Learning Using Programmable Network Devices: A Survey

Zheng, C., Hong, X., Ding, D., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3344351 · Cited by 98

Performance evaluation of secured network traffic classification using a machine learning approach

Afuwape, A. A., Xu, Y., Anajemba, J. H., & Srivastava, G. (2021) · Computer Standards & Interfaces · doi:10.1016/j.csi.2021.103545 · Cited by 83

A Few-Shot Class-Incremental Learning Method for Network Intrusion Detection

Du, L., Gu, Z., Wang, Y., Wang, L., & Jia, Y. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3332284 · Cited by 83

A Dive into the Dark Web: Hierarchical Traffic Classification of Anonymity Tools

Montieri, A., Ciuonzo, D., Bovenzi, G., Persico, V., & Pescapè, A. (2019) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2019.2901994 · Cited by 82

Novel feature selection and classification of Internet video traffic based on a hierarchical scheme

Dong, Y., Zhao, J., & Jin, J. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.03.019 · Cited by 78

Classifying the traffic state of urban expressways: A machine-learning approach

Cheng, Z., Wang, W., Lu, J. J., & Xing, X. (2018) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2018.10.035 · Cited by 77

MATEC: A lightweight neural network for online encrypted traffic classification

Cheng, J., Wu, Y., Yuepeng, E., You, J., Li, T., Li, H., & Ge, J. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108472 · Cited by 74

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

Application Domain-Based Overview of IoT Network Traffic Characteristics

Pekár, A., Mocnej, J., Seah, W. K. G., & Zolotová, I. (2020) · ACM Computing Surveys · doi:10.1145/3399669 · Cited by 77

ATVITSC: A Novel Encrypted Traffic Classification Method Based on Deep Learning

Liu, Y., Wang, X., Qu, B., & Zhao, F. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3433446 · Cited by 79

On Internet Traffic Classification: A Two-Phased Machine Learning Approach

Bakhshi, T., & Ghita, B. (2016) · Journal of Computer Networks and Communications · doi:10.1155/2016/2048302 · Cited by 76

Unsupervised Traffic Flow Classification Using a Neural Autoencoder

Höchst, J., Baumgärtner, L., Hollick, M., & Freisleben, B. (2017) · Conference on Local Computer Networks · doi:10.1109/lcn.2017.57 · Cited by 71

Internet Censorship detection: A survey

Aceto, G., & Pescapè, A. (2015) · Computer Networks · doi:10.1016/j.comnet.2015.03.008 · Cited by 69

Understanding the Modeling of Computer Network Delays using Neural Networks

Mestres, A., Alarcón, E., Ji, Y., & Cabellos‐Aparicio, A. (2018) · doi:10.1145/3229607.3229613 · Cited by 69

Multi-Scale LSTM Model for BGP Anomaly Classification

Cheng, M., Li, Q., Lv, J., Liu, W., & Wang, J. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2018.2824809 · Cited by 70

Real Network Traffic Collection and Deep Learning for Mobile App Identification

Wang, X., Chen, S., & Su, J. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/4707909 · Cited by 70

CBSeq: A Channel-Level Behavior Sequence for Encrypted Malware Traffic Detection

Cui, S., Dong, C., Shen, M., Liu, Y., Jiang, B.-S., & Lu, Z.-G. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3300521 · Cited by 52

Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal

Gómez, S. E., Carro, B., Sánchez-Esguevillas, A., & Hernández-Callejo, L. (2017) · Computer Networks · doi:10.1016/j.comnet.2017.07.018 · Cited by 65

Detection of Encrypted Cryptomining Malware Connections With Machine and Deep Learning

Pastor, A., Mozó, A., Vakaruk, S., Canavese, D., Lopez, D. R., Regano, L., Gómez-Canaval, S., & Lioy, A. (2020) · IEEE Access · doi:10.1109/access.2020.3019658 · Cited by 63

An accurate traffic classification model based on support vector machines

Cao, J., Fang, Z., Qu, G., Sun, H., & Zhang, D. (2017) · International Journal of Network Management · doi:10.1002/nem.1962 · Cited by 63

Traffic identification engine: an open platform for traffic classification

de Donato, W., Pescapè, A., & Dainotti, A. (2014) · IEEE Network · doi:10.1109/mnet.2014.6786614 · Cited by 62

Application traffic classification at the early stage by characterizing application rounds

Huang, N.-F., Jai, G.-Y., Chao, H., Tzang, Y., & Chang, H. (2013) · Information Sciences · doi:10.1016/j.ins.2012.12.039 · Cited by 62

Anomaly detection in traffic using L1-norm minimization extreme learning machine

Wang, Y., Li, D., Du, Y., & Pan, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.04.073 · Cited by 61

Is Our Ground-Truth for Traffic Classification Reliable?

Carela-Español, V., Bujlow, T., & Barlet‐Ros, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-04918-2_10 · Cited by 61

A Framework for More Effective Dark Web Marketplace Investigations

Hayes, D. R., Cappa, F., & Cardon, J. (2018) · Information · doi:10.3390/info9080186 · Cited by 60

Machine Learning for Traffic Analysis: A Review

Alqudah, N., & Yaseen, Q. M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.111 · Cited by 60

A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection

Nascita, A., Aceto, G., Ciuonzo, D., Montieri, A., Persico, V., & Pescapè, A. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3504955 · Cited by 79

BFCN: A Novel Classification Method of Encrypted Traffic Based on BERT and CNN

Shi, Z., Luktarhan, N., Song, Y., & Tian, G. (2023) · Electronics · doi:10.3390/electronics12030516 · Cited by 61

Network Flow Watermarking: A Survey

Iacovazzi, A., & Elovici, Y. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2604405 · Cited by 59

IIsy: Hybrid In-Network Classification Using Programmable Switches

Zheng, C., Xiong, Z., Bui, T. T., Kaupmees, S., Bensoussane, R., Bernabeu, A., Vargaftik, S., Ben-Itzhak, Y., & Zilberman, N. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3364757 · Cited by 107

A multilevel taxonomy and requirements for an optimal traffic‐classification model

Khalife, J., Hajjar, A. F., & Dı́az-Verdejo, J. E. (2014) · International Journal of Network Management · doi:10.1002/nem.1855 · Cited by 58

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

Detection of Encrypted Malicious Network Traffic using Machine Learning

Lucia, M. J. D., & Cotton, C. (2019) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom47813.2019.9020856 · Cited by 56

Certificate-aware encrypted traffic classification using Second-Order Markov Chain

Shen, M., Wei, M., Zhu, L., Wang, M., & Li, F. (2016) · International Workshop on Quality of Service · doi:10.1109/iwqos.2016.7590451 · Cited by 55

Exploratory study on Class Imbalance and solutions for Network Traffic Classification

Gómez, S. E., Hernández-Callejo, L., Carro, B., & Sánchez-Esguevillas, A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.07.091 · Cited by 55

Analyzing HTTPS encrypted traffic to identify user's operating system, browser and application

Muehlstein, J., Zion, Y., Bahumi, M., Kirshenboim, I., Dubin, R., Dvir, A. Z., & Pele, O. (2017) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2017.8013420 · Cited by 62

Fairness-Driven Queue Management: A Survey and Taxonomy

Abbas, G., Halim, Z., & Abbas, Z. H. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2463121 · Cited by 54

SoK: Making Sense of Censorship Resistance Systems

Khattak, S., Elahi, T., Simon, L., Swanson, C. M., Murdoch, S. J., & Goldberg, I. (2016) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2016-0028 · Cited by 54

Effective packet number for early stage internet traffic identification

Peng, L., Yang, B., & Chen, Y. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.12.053 · Cited by 52

A Session-Packets-Based Encrypted Traffic Classification Using Capsule Neural Networks

Cui, S., Jiang, B., Cai, Z., Lü, Z., Liu, S., & Liu, J. (2019) · 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/smartcity/dss.2019.00071 · Cited by 52

Imbalanced traffic identification using an imbalanced data gravitation-based classification model

Peng, L., Zhang, H., Chen, Y., & Yang, B. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.05.010 · Cited by 51

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 Hybrid Analysis-Based Approach to Android Malware Family Classification

Ding, C. Y., Luktarhan, N., Lu, B., & Zhang, W. (2021) · Entropy · doi:10.3390/e23081009 · Cited by 58

Effectiveness of Statistical Features for Early Stage Internet Traffic Identification

Peng, L., Yang, B., Chen, Y., & Chen, Z. (2015) · International Journal of Parallel Programming · doi:10.1007/s10766-014-0337-2 · Cited by 49

Internet traffic classification based on flows' statistical properties with machine learning

Vlăduţu, A., Comăneci, D., & Dobre, C. (2016) · International Journal of Network Management · doi:10.1002/nem.1929 · Cited by 48

CSCNN: Cost-Sensitive Convolutional Neural Network for Encrypted Traffic Classification

Soleymanpour, S., Sadr, H., & Soleimandarabi, M. N. (2021) · Neural Processing Letters · doi:10.1007/s11063-021-10534-6 · Cited by 47

Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Bovenzi, G., Nascita, A., Yang, L., Finamore, A., Aceto, G., Ciuonzo, D., Pescapè, A., & Rossi, D. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3287430 · Cited by 48

A Network Traffic Classification Method Based on Dual-Mode Feature Extraction and Hybrid Neural Networks

Yang, Y., Yan, Y., Gao, Z., Rui, L., Lyu, R., Gao, B., & Yu, P. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3262246 · Cited by 48

Intelligent Traffic Management in Next-Generation Networks

Aouedi, O., Piamrat, K., & Parrein, B. (2022) · Future Internet · doi:10.3390/fi14020044 · Cited by 45

Comparative Study of CNN and RNN for Deep Learning Based Intrusion Detection System

Cui, J., Long, J., Min, E., Liu, Q., & Li, Q. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00018-9_15 · Cited by 47

Traffic classification: Issues and challenges

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