Developing a traffic classification platform for enterprise networks with SDN: Experiences &amp;amp; lessons learned

Ng, Bryan; Hayes, Matthew; Seah, Winston K.G.

doi:10.1109/ifipnetworking.2015.7145322

Cited by 38 publications

(26 citation statements)

References 16 publications

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“…Another work [25] represents a design of an SDN-based traffic classification platform for an enterprise network. The main difference to our work is that it focuses on port-based and deep packet inspection for traffic classification, while we classify network flows basing on port-based and deep packet inspection and additionally on information extracted from packet headers.…”

Section: Application-aware and Traffic Classification Solutions In Sdnmentioning

confidence: 99%

Application-Aware Firewall Mechanism for Software Defined Networks

Nife

Kotulski

2020

J Netw Syst Manage

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Software-Defined-Networking (SDN) has been recently arising as a new technology in the IT industry. It is a network architecture that hopes to provide better solutions to most of the constraints in contemporary networks. SDN is a centralized control architecture for networking in which the control plane is separated from the data plane, the network services are abstracted from the underlying forwarding devices, and the network's intelligence is centralized in a software-based directly-programmed device called a controller. These features of SDN provide more flexible, programmable and innovative network's architecture. However, they may pose new vulnerabilities and may lead to new security problems. In this paper, we propose the application-aware firewall mechanism for SDN, which can be implemented as an extension to the network's controller. In order to provide more control and visibility in applications running over the network, the system is able to detect network applications that may at some point affect network's performance, and it is capable to dynamically enforce constraint rules on applications. The firewall architecture is designed as four cooperating modules: the Main Module, the Filtering Module, the Application Identification Module, and the Security-Enforcement Module. The proposed mechanism checks the network traffic at the network, transport, and application levels, and installs appropriate security instructions down into the network. The proposed solution features were implemented and tested using a Python-based POX controller, and the network topology was built using Mininet emulation tool.

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Section: Application-aware and Traffic Classification Solutions In Sdnmentioning

confidence: 99%

Application-Aware Firewall Mechanism for Software Defined Networks

Nife

Kotulski

2020

J Netw Syst Manage

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“…Intuitively, the average load of all resources can be used as a solution. However, the problems that will exist include the following: it does not combine the actual configuration of the servers and it will take resource fragmentation; besides, much information about request network flow is not to be learned and considered (required type, traffic volume, and so on) [20,21].…”

Section: Problem Description and Modelmentioning

confidence: 99%

Proactive VNF Scaling with Heterogeneous Cloud Resources: Fusing Long Short-Term Memory Prediction and Cooperative Allocation

2020

Mathematical Problems in Engineering

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Network function virtualization (NFV) is designed to implement network functions by software that replaces proprietary hardware devices in traditional networks. In response to the growing demand of resource-intensive services, for NFV cloud service providers, software-oriented network functions face a number of challenges, such as dynamic deployment of virtual network functions and efficient allocation of multiple resources. This study aims at the dynamic allocation and adjustment of network multiresources and multitype flows for NFV. First, to seek a proactive approach to provision new instances for overloaded VNFs ahead of time, a model called long short-term memory recurrent neural network (LSTM RNN) is proposed to estimate flows in this paper. Then, based on the estimated flow, a cooperative and complementary resource allocation algorithm is designed to reduce resource fragmentation and improve the utilization. The final results demonstrate the advantage of the LSTM model on predicting the network function flow requirements, and our algorithm achieves good results and performance improvement in dynamically expanding network functions and improving resource utilization.

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“…Another emerging approach is the use of Software-defined networks(SDNs) in satelliteterrestrial networks. In SDNs, traffic classification can be easily deployed in the SDN' master controllers as it is exposed in [8,1].…”

Section: Introductionmentioning

confidence: 99%

A Wearable Machine Learning Solution for Internet Traffic Classification in Satellite Communications

Pacheco

Expósito

Gineste³

2019

Service-Oriented Computing

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In this paper, we present an architectural framework to perform Internet traffic classification in Satellite Communications for QoS management. Such a framework is based on Machine Learning techniques. We propose the elements that the framework should include, as well as an implementation proposal. We define and validate some of its elements by evaluating an Internet dataset generated on an emulated Satellite Architecture. We also outline some discussions and future works that should be addressed to have an accurate Internet classification system.

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Developing a traffic classification platform for enterprise networks with SDN: Experiences & lessons learned

Cited by 38 publications

References 16 publications

Application-Aware Firewall Mechanism for Software Defined Networks

Application-Aware Firewall Mechanism for Software Defined Networks

Proactive VNF Scaling with Heterogeneous Cloud Resources: Fusing Long Short-Term Memory Prediction and Cooperative Allocation

A Wearable Machine Learning Solution for Internet Traffic Classification in Satellite Communications

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