Abstract
The network traffic classification task is focused on recognizing diverse kinds of applications or traffic data for which the received data packets are analysed that is essential in communication networks in these days. A network controller must have efficient understanding of applications and protocols in the network traffic to deploy the suitable security solutions. In addition, the name or kind of application is recognized and classified in the network for treating some aspects in advance. The process to classify the network traffic has become popular among research community along with the industrial field. A number of schemes have been put forward and constructed over the last two decades. The network traffic can be classified in several stages, in which pre-processing is done, attributes are extracted and classification is performed. The various machine learning models are reviewed in this paper for the network traffic classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hasibi R, Shokri M, Fooladi MDT (2019) Augmentation scheme for dealing with imbalanced network traffic classification using deep learning. arXiv:1901.00204v1
Shafiq M, Yu X, Bashir AK, Chaudhry HN, Wang D (2018) A machine learning approach for feature selection traffic classification using security analysis. J Supercomput 74:4867–4892
Peng L, Zhang H, Chen Y, Yang Bo (2017) Imbalanced traffic identification using an imbalanced data gravitation-based classification model. Comput Commun 102:177–189
Vu L, Bui CT, Nguyen QU (2018) A deep learning based method for handling imbalanced problem in network traffic classification. In: The eighth international symposium, vol 15, pp 3478–3485
Tanha J, Abdi Y, Samadi N, Razzaghi N, Asadpour M (2020) Boosting methods for multi class imbalanced data classification: an experimental review. J Big Data 4:6754–6762
Liu Q, Liu Z (2014) A comparison of improving multi-class imbalance for internet traffic classification. Inf Syst Front 8:5432–5440
Zhena L, Qiong L (2016) A new feature selection method for internet traffic classification using ML. In: International conference on medical physics and biomedical engineering, vol 9, pp 9654–9663
Yang J, Wang Y, Dong C, Cheng G (2012) The evaluation measure study in network traffic multi-class classification based on AUC. In: International conference on ICT convergence (ICTC), vol 21, pp 362–367
Dhote Y, Agrawal S, Deen AJ (2015) A survey on feature selection techniques for internet traffic classification. In: International conference on computational intelligence and communication networks (CICN), vol 5, pp 1375–1380
Wang Z, Wang P, Zhou X, Li S, Zhang M (2019) FLOWGAN: unbalanced network encrypted traffic identification method based on GAN. In: IEEE international conference on parallel and distributed processing with applications, big data and cloud computing, sustainable computing and communications, vol 11, pp 975–983
Sharif MS, Moein M (2021) An effective cost-sensitive convolutional neural network for network traffic classification. In: International conference on innovation and intelligence for informatics, computing, and technologies (3ICT), vol 21, pp 40–45
Jiang K, Wang W, Wang A, Wu H (2020) Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access 8:32464–32476
Sadeghzadeh AM, Shiravi S, Jalili R (2021) Adversarial network traffic: towards evaluating the robustness of deep-learning-based network traffic classification. IEEE Trans Netw Serv Manag 18:1962–1976
Bu Z, Zhou B, Cheng P, Zhang K, Ling Z-H (2020) Encrypted network traffic classification using deep and parallel network-in-network models. IEEE Access 8:132950–132959
Chen W, Lyu F, Fan Wu, Yang P, Xue G, Li M (2021) Sequential message characterization for early classification of encrypted internet traffic. IEEE Trans Veh Technol 70:3746–3760
Wang X, Wang X, Jin L, Lv R, Dai B, He M, Lv T (2021) Evolutionary algorithm-based and network architecture search-enabled multiobjective traffic classification. IEEE Access 9:52310–52325
Alizadeh H, Vranken H, Zúquete A, Miri A (2020) Timely classification and verification of network traffic using Gaussian mixture models. IEEE Access 8:91287–91302
Iliyasu AS, Deng H (2020) Semi-supervised encrypted traffic classification with deep convolutional generative adversarial networks. IEEE Access 8:118–126
Shapira T, Shavitt Y (2021) FlowPic: a generic representation for encrypted traffic classification and applications identification. IEEE Trans Netw Serv Manage 18:1218–1232
Yoo J, Min B, Kim S, Shin D, Shin D (2021) Study on network intrusion detection method using discrete pre-processing method and convolution neural network. IEEE Access 9:142348–142361
Mezina A, Burget R, Travieso-González CM (2021) Network anomaly detection with temporal convolutional network and U-Net model. IEEE Access 9:143608–143622
Hu X, Gu C, Wei F (2021) CLD-Net: a network combining CNN and LSTM for internet encrypted traffic classification. Secur Commun Netw 12:138502–138510
Bei Lu, Luktarhan N, Ding C, Zhang W (2021) ICLSTM: encrypted traffic service identification based on inception-LSTM neural network. Symmetry 13:1080–1087
Gómez SE, Hernández-Callejo L, Sánchez-Esguevillas AJ (2019) Exploratory study on class imbalance and solutions for network traffic classification. Neurocomputing 343:100–119
Guo Y, Li Z, Li Z, Xiong G, Jiang M, Gou G (2020) FLAGB: focal loss based adaptive gradient boosting for imbalanced traffic classification. In: International joint conference on neural networks (IJCNN), pp 1–8
Dong S (2021) Multi class SVM algorithm with active learning for network traffic classification. Expert Syst Appl 176
Peng L, Zhang H, Yang Bo (2017) Imbalanced traffic identification using an imbalanced data gravitation-based classification model. Comput Commun 102:177–189
Saber MAS, Ghorbani M, Bayati A, Nguyen K-K, Cheriet M (2020) Online data center traffic classification based on inter-flow correlations. IEEE Access 8:60401–60416
Wang P, Li S, Ye F, Wang Z, Zhang M (2020) PacketCGAN: exploratory study of class imbalance for encrypted traffic classification using CGAN. In: IEEE international conference on communications (ICC), pp 1–7
Song M, Ran J, Li S (2019) Encrypted traffic classification based on text convolution neural networks. In: IEEE 7th international conference on computer science and network technology (ICCSNT), pp 432–436
Guo Y, Xiong G, Li Z, Shi J, Cui M, Gou G (2021) Combating imbalance in network traffic classification using GAN based oversampling. In: IFIP networking conference (IFIP networking), pp 1–9
Wang ZX, Wang P, Zhou X, Li SH, Zhang M (2019) FLOWGAN: unbalanced network encrypted traffic identification method based on GAN. In: IEEE international conference on parallel and distributed processing with applications, big data and cloud computing, sustainable computing and communications, social computing and networking (ISPA/BDCloud/SocialCom/SustainCom), pp 18–25
Lopez-Martin M, Sanchez-Esguevillas A, Arribas JI, Carro B (2021) Network intrusion detection based on extended RBF neural network with offline reinforcement learning. IEEE Access 9:153153–153170
Pan T, Chen J, Xie J, Zhou Z, He S (2021) Deep feature generating network: a new method for intelligent fault detection of mechanical systems under class imbalance. IEEE Trans Ind Inf 17:6282–6293
Oeung P, Shen F (2019) Imbalanced internet traffic classification using ensemble framework. In: International conference on information networking (ICOIN), pp 37–42
Zaki FAM, Chin TS (2019) FWFS: selecting robust features towards reliable and stable traffic classifier in SDN. IEEE Access 7:166011–166020
Xu L, Zhou X, Ren Y, Qin Y (2019) A traffic classification method based on packet transport layer payload by ensemble learning. In: IEEE symposium on computers and communications (ISCC), pp 1–6
Wang W, Zhu M, Zeng X, Ye X, Sheng Y (2017) Malware traffic classification using convolutional neural network for representation learning. In: International conference on information networking (ICOIN), pp 712–717
Liu L, Wang P, Lin J, Liu L (2021) Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access 9:7550–7563
Shang Q, Feng L, Gao S (2021) A hybrid method for traffic incident detection using random forest-recursive feature elimination and long short-term memory network with Bayesian optimization algorithm. IEEE Access 9:1219–1232
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhatla, N., Malik, M. (2023). Network Traffic Classification Techniques: A Review. In: Chatterjee, P., Pamucar, D., Yazdani, M., Panchal, D. (eds) Computational Intelligence for Engineering and Management Applications. Lecture Notes in Electrical Engineering, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-19-8493-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-19-8493-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-8492-1
Online ISBN: 978-981-19-8493-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)