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.

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Adversarial Network Traffic: Towards Evaluating the Robustness of Deep-Learning-Based Network Traffic Classification

IEEE Transactions on Network and Service Management ◽

10.1109/tnsm.2021.3052888 ◽

2021 ◽

Vol 18 (2) ◽

pp. 1962-1976

Author(s):

Amir Mahdi Sadeghzadeh ◽

Saeed Shiravi ◽

Rasool Jalili

Keyword(s):

Deep Learning ◽

Network Traffic ◽

Traffic Classification ◽

Adversarial Network ◽

Network Traffic Classification

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Packet Vision: a convolutional neural network approach for network traffic classification

2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) ◽

10.1109/sibgrapi51738.2020.00042 ◽

2020 ◽

Author(s):

Rodrigo Moreira ◽

Larissa Ferreira Rodrigues ◽

Pedro Frosi Rosa ◽

Rui L. Aguiar ◽

Flavio de Oliveira Silva

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Traffic ◽

Traffic Classification ◽

Network Approach ◽

Neural Network Approach ◽

Network Traffic Classification

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Multi Class SVM Algorithm with Active Learning for Network Traffic Classification

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114885 ◽

2021 ◽

pp. 114885

Author(s):

Shi Dong

Keyword(s):

Active Learning ◽

Network Traffic ◽

Traffic Classification ◽

Svm Algorithm ◽

Network Traffic Classification

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Network traffic classification via HMM under the guidance of syntactic structure

Computer Networks ◽

10.1016/j.comnet.2012.01.021 ◽

2012 ◽

Vol 56 (6) ◽

pp. 1814-1825 ◽

Cited By ~ 6

Author(s):

ChengGuo Yin ◽

ShuangQing Li ◽

Qi Li

Keyword(s):

Network Traffic ◽

Syntactic Structure ◽

Traffic Classification ◽

Network Traffic Classification

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A novel network traffic classification approach via discriminative feature learning

Proceedings of the 35th Annual ACM Symposium on Applied Computing ◽

10.1145/3341105.3373844 ◽

2020 ◽

Author(s):

Lixin Zhao ◽

Lijun Cai ◽

Aimin Yu ◽

Zhen Xu ◽

Dan Meng

Keyword(s):

Network Traffic ◽

Feature Learning ◽

Traffic Classification ◽

Classification Approach ◽

Discriminative Feature ◽

Network Traffic Classification

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MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Information ◽

10.3390/info9090233 ◽

2018 ◽

Vol 9 (9) ◽

pp. 233 ◽

Cited By ~ 1

Author(s):

Zuleika Nascimento ◽

Djamel Sadok

Keyword(s):

Machine Learning ◽

Network Traffic ◽

Machine Learning Algorithms ◽

Divide And Conquer ◽

Pareto Optimal ◽

Optimization Approach ◽

Traffic Classification ◽

Multi Objective ◽

Network Traffic Classification ◽

Changes Over Time

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

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