Machine Learning in Software Defined Networks: Data collection and traffic classification

Amaral, Pedro; Dinis, Jorge; Pinto, Paulo; Bernardo, Luís; Tavares, João Manuel R. S.; Mamede, Henrique São

doi:10.1109/icnp.2016.7785327

Cited by 150 publications

(98 citation statements)

References 16 publications

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“…In [34] the authors present an architecture to collect traffic data from OpenFlow switches and use the collected data to classify such traffic as belonging to certain well-known applications. In [35] the author uses machine learning and OpenFlow in the design and implementation of a traffic classification system that accurately classifies traffic without affecting the latency or bandwidth in the data plane.…”

Section: Related Workmentioning

confidence: 99%

Learning from Network Device Statistics

2017

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We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are ''encoded'' in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .

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Section: Related Workmentioning

confidence: 99%

Learning from Network Device Statistics

2017

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“…To achieve the application awareness, the C4.5 decision tree algorithm was used to build the application classifier, and the machine learning–based trainer and classifier were integrated in the controller because of a global view and the logically centralized control capability. Amaral et al presented a novel machine learning–based data collection and traffic classification architecture, which could be applied to legacy network and SDN network. In this architecture, the controller collected the flow statistics from the switches and then used the supervised machine learning algorithm to classify traffic.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In recent years, many research works have already applied machine learning methods in network application classification. [2][3][4][5][6][7][8][9][10][11]23,24 Most of them are focused on improving the machine learning algorithm and feature selection since the selected features and machine learning algorithm have a great effect on the classifier's performance.…”

Section: Related Workmentioning

confidence: 99%

“…To achieve accurate network applications, a large number of different application classification methods have been proposed in the past few decades, mainly including port‐based approach, payload‐based approach, and machine learning–based approach . In current years, the most widely used technology for application classification is the machine learning–based approach, in which backpropagation (BP) neural network, Bayesian network, support vector machine (SVM), and C4.5 decision tree are usually applied to the classification …”

Section: Introductionmentioning

confidence: 99%

“…2 In current years, the most widely used technology for application classification is the machine learning-based approach, in which backpropagation (BP) neural network, Bayesian network, support vector machine (SVM), and C4.5 decision tree are usually applied to the classification. [3][4][5][6][7][8][9][10][11] Different from identifying port number in the port-based approach as well as inspecting payload content in the payload-based approach, in machine learning-based classification methods, the flow features statistics, including the size of packet, interpacket time, and flow duration time, are used to automatically identify and classify network application by using machine learning. The shallow neural network is generally used to build the application classifier in lots of machine learning-based classification methods.…”

Section: Introductionmentioning

confidence: 99%

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Accurate application classification is important and useful to improve network performance. However, with the continuous expansion of network scale and the rapid increase of network users, it is very difficult for the existing application classification methods to accurately identify and classify network applications. Currently, most classification methods are suitable for small‐scale data sets and cannot achieve high classification accuracy because of the shallow learning structure and the limited learning ability. The emergence of deep learning technology and software‐defined networking (SDN) enables the application classification method to process large‐scale data. In this paper, by leveraging the SDN architecture, we present a novel hybrid deep neural network–based application classification method, which achieves high classification accuracy without the manual feature selection and extraction. In the proposed application classification framework, by taking the advantage of the logical centralized control and powerful computing capability, the massive network traffic is easily collected and processed by the SDN controller. The processed data is used to train the hybrid deep neural network, which is composed of the stacked autoencoder and softmax regression layer. The deep flow features can be obtained from the stacked autoencoder automatically instead of the manual feature selection and extraction. The softmax regression layer is used as the classifier to realize the application classification. Finally, simulation results demonstrate that our proposed classification method is effective and gets higher classification accuracy than the support vector machine–based classification method.

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As the number of smartphones and demand for higher data-rate connections keep explosively growing, the technology has to pursue this trend in order to be able to provide the suitable communication schemes. By 2020, it is calculated that the total global mobile wireless user devices will be over 10 billion, with the mobile data traffic growing more than 200 times over compared to 2010 numbers. It is foreseeable that 4G mobile communication system could no longer meet the need of users service requirements. Thus, the new 5G communication systems are promising a complete network structure with unlimited access to information, providing the requested service demands to users far beyond what the current 4G offers by supporting innovative new wireless technologies and network architecture to meet the extremely high-performance requirement (Table 1.1).These features include support for new types and massive number of devices, for very high mobile traffic volumes, universal access for users, very high frequency reuse and spectrum reuse in wireless technologies, automated provisioning, configuration and management of a wide range of new network services, ultrareliable, ultralow latency, ultradensification, and even more. 5G networks would be a heterogeneous networks (HetNets), meaning that different networks will be integrated all together to a unified system, enabling

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Machine Learning in Software Defined Networks: Data collection and traffic classification

Cited by 150 publications

References 16 publications

Learning from Network Device Statistics

Learning from Network Device Statistics

Deep learning–based network application classification for SDN

Universal Access in 5G Networks: Potential Challenges and Opportunities for Urban and Rural Environments

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