scholarly journals Data Transformation Schemes for CNN-Based Network Traffic Analysis: A Survey

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2042
Author(s):  
Jacek Krupski ◽  
Waldemar Graniszewski ◽  
Marcin Iwanowski
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Three Dimensional ◽  
Data Transformation ◽  
Traffic Analysis ◽  
Traffic Classification ◽  
Cyber Attack ◽  
Network Traffic Classification ◽  
Scientific Papers ◽  
Analytical Approaches

The enormous growth of services and data transmitted over the internet, the bloodstream of modern civilization, has caused a remarkable increase in cyber attack threats. This fact has forced the development of methods of preventing attacks. Among them, an important and constantly growing role is that of machine learning (ML) approaches. Convolutional neural networks (CNN) belong to the hottest ML techniques that have gained popularity, thanks to the rapid growth of computing power available. Thus, it is no wonder that these techniques have started to also be applied in the network traffic classification domain. This has resulted in a constant increase in the number of scientific papers describing various approaches to CNN-based traffic analysis. This paper is a survey of them, prepared with particular emphasis on a crucial but often disregarded aspect of this topic—the data transformation schemes. Their importance is a consequence of the fact that network traffic data and machine learning data have totally different structures. The former is a time series of values—consecutive bytes of the datastream. The latter, in turn, are one-, two- or even three-dimensional data samples of fixed lengths/sizes. In this paper, we introduce a taxonomy of data transformation schemes. Next, we use this categorization to describe various CNN-based analytical approaches found in the literature.

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

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 233 ◽  
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.

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

2019 ◽  
Vol 21 (2) ◽  
pp. 1988-2014 ◽  
Author(s):  
Fannia Pacheco ◽  
Ernesto Exposito ◽  
Mathieu Gineste ◽  
Cedric Baudoin ◽  
Jose Aguilar
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Traffic Classification ◽  
Systematic Survey ◽  
Network Traffic Classification

scholarly journals Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Muhammad Shafiq ◽  
Xiangzhan Yu
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Network Traffic ◽  
Early Stage ◽  
Statistical Tests ◽  
Internet Traffic ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Traffic Classification ◽  
Network Traffic Classification

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet size of 20 packets and then mutual information analysis is carried out to find out the mutual information of each packet onnflow type. Thereafter, we execute 10 well-known machine learning algorithms using crossover classification method. Two statistical analysis tests, Friedman and Wilcoxon pairwise tests, are applied for the experimental results. Moreover, we also apply the statistical tests for classifiers to find out effective ML classifier. Our experimental results show that 13–19 packets are the effective packet numbers for 5G IM WeChat application at early stage network traffic classification. We also find out effective ML classifier, where Random Forest ML classifier is effective classifier at early stage Internet traffic classification.

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