Improving the network traffic classification using the Packet Vision approach
The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.
Hyun-Kyo Lim, Ju-Bong Kim, Kwihoon Kim, Yong-Geun Hong, and Youn-Hee Han. Payload-based traffic classification using multi-layer lstm in software defined networks. Applied Sciences, 9(12):2550, 2019.
T. T. T. Nguyen and G. Armitage. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys Tutorials, 10(4):56–76, 2008.
Larissa Ferreira Rodrigues, Murilo Coelho Naldi, and João Fernando Mari. Comparing convolutional neural networks and preprocessing techniques for hep-2 cell classification in immunofluorescence images. Computers in Biology and Medicine, 116:103542, 2020.
Yukiko Nagao, Mika Sakamoto, Takumi Chinen, Yasushi Okada, and Daisuke Takao. Robust classification of cell cycle phase and biological feature extraction by image-based deep learning. Molecular Biology of the Cell, 31(13):1346–1354, 2020. PMID: 32320349.
Keiller Nogueira, Otávio A.B. Penatti, and Jefersson A. [dos Santos]. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61:539 – 556, 2017.
Yanming Guo, Yu Liu, Ard Oerlemans, Songyang Lao, Song Wu, and Michael S. Lew. Deep learning for visual understanding: A review. Neurocomputing, 187:27 – 48, 2016. Recent Developments on Deep Big Vision.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse. Everything you wanted to know about deep learning for computer vision but were afraid to ask. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pages 17–41, Oct 2017.
S. Potluri, A. Fasih, L. K. Vutukuru, F. A. Machot, and K. Kyamakya. Cnn based high performance computing for real time image processing on gpu. In Proceedings of the Joint INDS’11 ISTET’11, pages 1–7, 2011.
S. Shi, Q. Wang, P. Xu, and X. Chu. Benchmarking state-of-the-art deep learning software tools. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pages 99–104, 2016.
Jeffrey Erman, Martin Arlitt, and Anirban Mahanti. Traffic classification using clustering algorithms. In Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, MineNet ’06, page 281–286, New York, NY, USA, 2006. Association for Computing Machinery.
Danish Vasan, Mamoun Alazab, Sobia Wassan, Hamad Naeem, Babak Safaei, and Qin Zheng. Imcfn: Image-based malware classification using fine-tuned convolutional neural network architecture. Computer Networks, 171:107138, 2020.
Z. Chen, K. He, J. Li, and Y. Geng. 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), pages 1271–1276, 2017.
S. Rezaei and X. Liu. Deep learning for encrypted traffic classification: An overview. IEEE Communications Magazine, 57(5):76–81, 2019.
M. Lopez-Martin, B. Carro, A. Sanchez-Esguevillas, and J. Lloret. Network traffic classifier with convolutional and recurrent neural networks for internet of things. IEEE Access, 5:18042–18050, 2017.
F. Al-Obaidy, S. Momtahen, M. F. Hossain, and F. Mohammadi. Encrypted traffic classification based ml for identifying different social media applications. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pages 1–5, 2019.
Murat Soysal and Ece Guran Schmidt. Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation, 67(6):451 – 467, 2010.
Rodrigo Moreira, Pedro Frosi Rosa, Rui Luis Andrade Aguiar, and Flávio de Oliveira Silva. Enabling multi-domain and end-to-end slice orchestration for virtualization everything functions (vxfs). In Leonard Barolli, Flora Amato, Francesco Moscato, Tomoya Enokido, and Makoto Takizawa, editors, Advanced Information Networking and Applications, pages 830–844, Cham, 2020. Springer International Publishing.
M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. Sadeghi, and S. Tarkoma. Iot sentinel: Automated device-type identification for security enforcement in iot. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pages 2177–2184, 2017.
Valentín Carela-Espa˜nol, Tomasz Bujlow, and Pere Barlet-Ros. Is our ground-truth for traffic classification reliable? In Michalis Faloutsos and Aleksandar Kuzmanovic, editors, Passive and Active Measurement, pages 98–108, Cham, 2014. Springer International Publishing.
T. Shapira and Y. Shavitt. Flowpic: Encrypted internet traffic classification is as easy as image recognition. In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 680–687, 2019.
L. Xu, X. Zhou, Y. Ren, and Y. Qin. A traffic classification method based on packet transport layer payload by ensemble learning. In 2019 IEEE Symposium on Computers and Communications (ISCC), pages 1–6, 2019.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, June 2016.
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and ¡0.5mb model size, 2016.
J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, June 2009.
Pierre A. Devijver and Josef Kittler. Pattern Recognition: A Statistical Approach. Prentice-Hall, 1982.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification (2Nd Edition). Wiley-Interscience, New York, NY, USA, 2000.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8026–8037. Curran Associates, Inc., 2019.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, Nov 1998.