Abstract
By the rapid development of the Internet and online applications, traffic classification not only has changed to an interesting topic in the field of computer networks but also plays a key role in cyber-security and network management. Although numerous studies have been conducted in recent years, encrypted traffic classification still remains a major challenge and unbalanced data is known as one of the most important problems in this field. Even though previous researches have focused on dealing with the class imbalance problem in the pre-processing step via machine learning and specifically deep learning methods, they are still confronted with some restrictions. To this end, a new traffic classification method is presented in this paper that aims to deal with the problem of unbalanced data along the training process. The proposed method utilized a Cost-Sensitive Convolution Neural Network (CSCNN) where a cost matrix was employed to assign a cost to each misclassification based on the distribution of each class. These costs were then utilized during the training process to increase the final classification accuracy. Various experiments were carried out to explore the performance of the proposed method for the tasks of traffic classification, traffic description, and application identification. According to the obtained results, CSCNN achieved higher efficiency compared to both machine learning and deep learning based methods on the ISCX VPN-nonVPN dataset.
Similar content being viewed by others
References
Lotfollahi M, Siavoshani MJ, Zade RSH, Saberian M (2020) Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Comput 24(3):1999–2012
Wang P, Chen X, Ye F, Sun Z (2019) A survey of techniques for mobile service encrypted traffic classification using deep learning. IEEE Access 7:54024–54033
D’Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction. J Netw Comput Appl 173:102890
Aceto G, Ciuonzo D, Montieri A, Pescapé A (2021) DISTILLER: Encrypted traffic classification via multimodal multitask deep learning. J Netw Comput Appl:102985
Dias KL, Pongelupe MA, Caminhas WM, de Errico L (2019) An innovative approach for real-time network traffic classification. Comput Netw 158:143–157
Soleymanpour S, Sadr H, Beheshti H An Efficient Deep Learning Method for Encrypted Traffic Classification on the Web. In: 2020 6th International Conference on Web Research (ICWR) (2020) IEEE, pp 209–216
Sadr H, Nazari Solimandarabi M, Mirhosseini Moghadam M (2017) Categorization of persian detached handwritten letters using intelligent combinations of classifiers. J Adv Comput Res 8(4):13–21
Sadr H, Pedram MM, Teshnehlab M (2021) Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis. J AI Data Mining. https://doi.org/10.22044/jadm.2021.9618.2100
Sadr H, Soleimandarabi MN, Pedram M, Teshnelab M Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms. In: 2019 5th International Conference on Web Research (ICWR) (2019) IEEE, pp 134–140
Höchst J, Baumgärtner L, Hollick M, Freisleben B Unsupervised traffic flow classification using a neural autoencoder. In (2017) IEEE 42nd Conference on Local Computer Networks (LCN), 2017. IEEE, pp 523–526
Bi Q, Zhang H, Qin K (2021) Multi-scale stacking attention pooling for remote sensing scene classification. Neurocomput 436:147–161
Wang Q, Huang W, Xiong Z, Li X (2020) Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification. IEEE Transactions on Neural Networks and Learning Systems
Jadidinejad AH, Sadr H (2015) Improving weak queries using local cluster analysis as a preliminary framework. Indian J Sci Technol 8(5):495–510
Sadr H, Nazari Solimandarabi M (2019) Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures. J Adv Comput Res 10(2):1–10
Sadr H, Pedram MM, Teshnehlab M (2019) A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks. Neural Process Lett:1–17
Wang Q, Liu S, Chanussot J, Li X (2018) Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans Geosci Remote Sens 57(2):1155–1167
Draper-Gil G, Lashkari AH, Mamun MSI, Ghorbani AA Characterization of encrypted and vpn traffic using time-related. In: Proceedings of the 2nd international conference on information systems security and privacy (ICISSP) (2016) pp 407–414
D’Alconzo A, Drago I, Morichetta A, Mellia M, Casas P (2019) A survey on big data for network traffic monitoring and analysis. IEEE Trans Netw Serv Manage 16(3):800–813
Qi Y, Xu L, Yang B, Xue Y, Li J Packet classification algorithms: From theory to practice. In: IEEE INFOCOM 2009, 2009. IEEE, pp 648–656
Dainotti A, Pescape A, Claffy KC (2012) Issues and future directions in traffic classification. IEEE Network 26(1):35–40
Madhukar A, Williamson C A longitudinal study of P2P traffic classification. In: 14th IEEE International Symposium on Modeling, Analysis, and Simulation (2006) IEEE, pp 179–188
Moore AW, Papagiannaki K Toward the accurate identification of network applications. In: International Workshop on Passive and Active Network Measurement (2005) Springer, pp 41–54
Sherry J, Lan C, Popa RA, Ratnasamy S, Blindbox: Deep packet inspection over encrypted traffic. In: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, 2015. pp 213–226
Hua N, Song H, Lakshman T Variable-stride multi-pattern matching for scalable deep packet inspection. In: IEEE INFOCOM 2009, 2009. IEEE, pp 415–423
Wang X, Jiang J, Tang Y, Liu B, Wang X, StriD²FA: Scalable Regular Expression Matching for Deep Packet Inspection. In: 2011 IEEE International Conference on Communications (ICC) (2011) IEEE, pp 1–5
Soleimandarabi MN, Mirroshandel SA (2015) A novel approach for computing semantic relatedness of geographic terms. Indian J Sci Technol 8(27):1–11
Piskac P, Novotny J Using of time characteristics in data flow for traffic classification. In: IFIP International Conference on Autonomous Infrastructure, Management and Security (2011) Springer, pp 173–176
Yildirim T, Radcliffe P VoIP traffic classification in IPSec tunnels. In: 2010 International Conference on Electronics and Information Engineering, 2010. IEEE, pp V1-151-V151-157
Crotti M, Dusi M, Gringoli F, Salgarelli L (2007) Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput Commun Rev 37(1):5–16
Wang X, Parish DJ Optimised multi-stage tcp traffic classifier based on packet size distributions. In: 2010 Third International Conference on Communication Theory, Reliability, and Quality of Service, 2010. IEEE, pp 98–103
Auld T, Moore AW, Gull SF (2007) Bayesian neural networks for internet traffic classification. IEEE Trans Neural Netw 18(1):223–239
Sun R, Yang B, Peng L, Chen Z, Zhang L, Jing S Traffic classification using probabilistic neural networks. In: 2010 Sixth International Conference on Natural Computation, 2010. IEEE, pp 1914–1919
Yamansavascilar B, Guvensan MA, Yavuz AG, Karsligil ME Application identification via network traffic classification. In: 2017 International Conference on Computing, Networking and Communications (ICNC) (2017) IEEE, pp 843–848
Chen Z, He K, Li J, Geng Y Seq2Img: A sequence-to-image based approach towards IP traffic classification using convolutional neural networks. In (2017) IEEE International Conference on Big Data (Big Data), 2017. IEEE, pp 1271–1276
Wang W, Sheng Y, Wang J, Zeng X, Ye X, Huang Y, Zhu M (2017) HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access 6:1792–1806
Wang Q, Wan J, Yuan Y (2017) Deep metric learning for crowdedness regression. IEEE Trans Circuits Syst Video Technol 28(10):2633–2643
Wang P, Ye F, Chen X, Qian Y (2018) Datanet: Deep learning based encrypted network traffic classification in sdn home gateway. IEEE Access 6:55380–55391
Lopez-Martin M, Carro B, Sanchez-Esguevillas A, Lloret J (2017) Network traffic classifier with convolutional and recurrent neural networks for Internet of Things. IEEE Access 5:18042–18050
Wang W, Zhu M, Wang J, Zeng X, Yang Z End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In (2017) IEEE International Conference on Intelligence and Security Informatics (ISI), 2017. IEEE, pp 43–48
Krawczyk B, Woźniak M, Schaefer G (2014) Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl Soft Comput 14:554–562
Chung Y-A, Lin H-T, Yang S-W (2015) Cost-aware pre-training for multiclass cost-sensitive deep learning. arXiv preprint arXiv:151109337
Buda M, Maki A, Mazurowski MA (2018) A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw 106:249–259
Wang S, Liu W, Wu J, Cao L, Meng Q, Kennedy PJ Training deep neural networks on imbalanced data sets. In (2016) international joint conference on neural networks (IJCNN), 2016. IEEE, pp 4368–4374
Khan SH, Hayat M, Bennamoun M, Sohel FA, Togneri R (2017) Cost-sensitive learning of deep feature representations from imbalanced data. IEEE Trans Neural Netw Learn syst 29(8):3573–3587
Telikani A, Gandomi AH (2019) Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things. Internet of Things:100122
Sadr H, Solimandarabi MN, Pedram MM, Teshnehlab M (2021) A Novel Deep Learning Method for Textual Sentiment Analysis. arXiv preprint arXiv:210211651
Wang Q, Wan J, Yuan Y (2018) Locality constraint distance metric learning for traffic congestion detection. Pattern Recogn 75:272–281
Sadr H, Pedram MM, Teshnehlab M (2020) Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis. IEEE Access 8:86984–86997
Sadr H, Pedram MM, Teshnelab M (2019) Improving the performance of text sentiment analysis using deep convolutional neural Network Integrated with Hierarchical attention layer. Int J Inf Commun Technol Res 11(3):57–67
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Soleymanpour, S., Sadr, H. & Nazari Soleimandarabi, M. CSCNN: Cost-Sensitive Convolutional Neural Network for Encrypted Traffic Classification. Neural Process Lett 53, 3497–3523 (2021). https://doi.org/10.1007/s11063-021-10534-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-021-10534-6