Network Traffic Classification using Machine Learning Techniques over Software Defined Networks

Parsaei, Mohammad Reza; Sobouti, Mohammad Javad; Khayami, Seyed Raouf; Javidan, Reza

doi:10.14569/ijacsa.2017.080729

Cited by 28 publications

(5 citation statements)

References 19 publications

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“…These are feedforward, multilayer perceptron (MLP), NARX (Levenberg-Marquardt), and NARX (NB). The results were good with more than 95% identification [26].…”

Section: Literature Reviewmentioning

confidence: 83%

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

Alhayani,

R. Murphy

2023

Computers, Materials &Amp; Continua

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The Internet service provider (ISP) is the heart of any country's Internet infrastructure and plays an important role in connecting to the World Wide Web. Internet exchange point (IXP) allows the interconnection of two or more separate network infrastructures. All Internet traffic entering a country should pass through its IXP. Thus, it is an ideal location for performing malicious traffic analysis. Distributed denial of service (DDoS) attacks are becoming a more serious daily threat. Malicious actors in DDoS attacks control numerous infected machines known as botnets. Botnets are used to send numerous fake requests to overwhelm the resources of victims and make them unavailable for some periods. To date, such attacks present a major devastating security threat on the Internet. This paper proposes an effective and efficient machine learning (ML)-based DDoS detection approach for the early warning and protection of the Saudi Arabia Internet exchange point (SAIXP) platform. The effectiveness and efficiency of the proposed approach are verified by selecting an accurate ML method with a small number of input features. A chi-square method is used for feature selection because it is easier to compute than other methods, and it does not require any assumption about feature distribution values. Several ML methods are assessed using holdout and 10-fold tests on a public large-size dataset. The experiments showed that the performance of the decision tree (DT) classifier achieved a high accuracy result (99.98%) with a small number of features (10 features). The experimental results confirm the applicability of using DT and chi-square for DDoS detection and early warning in SAIXP.

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“…These are feedforward, multilayer perceptron (MLP), NARX (Levenberg-Marquardt), and NARX (NB). The results were good with more than 95% identification [26].…”

Section: Literature Reviewmentioning

confidence: 83%

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

Alhayani,

R. Murphy

2023

Computers, Materials &Amp; Continua

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“…This section presents the related works that used machine and deep learning in SDN traffic classification. Reza et al [8] used four variants of neural network estimators to classify traffic by applications. The estimators are feedforward neural network, multilayer Perceptron, non-linear autoregressive exogenous multilayer perceptron nonlinear autoregressive exogenous (NARX), and NARX (Naïve Bayes).…”

Section: Related Workmentioning

confidence: 99%

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

Samaan

Jeiad

2023

IJ-AI

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Precise traffic classification is essential to numerous network functionalities such as routing, network management, and resource allocation. Traditional classification techniques became insufficient due to the massive growth of network traffic that requires high computational costs. The arising model of software defined networking (SDN) has adjusted the network architecture to get a centralized controller that preserves a global view over the entire network. This paper proposes a model for SDN traffic classification based on machine learning (ML) using the Spark framework. The proposed model consists of two phases; learning and deployment. A ML pipeline is constructed in the learning phase, consisting of a set of stages combined as a single entity. Three ML models are built and evaluated; decision tree, random forest, and logistic regression, for classifying a well-known 75 applications, including Google and YouTube, accurately and in a short time scale. A dataset consisting of 3,577,296 flows with 87 features is used for training and testing the models. The decision tree model is elected for deployment according to the performance results, which indicate that it has the best accuracy with 0.98. The performance of the proposed model is compared with the state-of-the-art works, and better accuracy result is reported.

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“…The most prevalent type of supervised learning is the deep neural network (DNN). Studies [12,13] have also proposed the use of DNNs for traffic classification, based on which the routing of the traffic is adjusted. Mao et al [14] have proposed a routing mechanism using a deep belief network (DBN).…”

Section: Related Workmentioning

confidence: 99%

An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization

et al. 2022

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Routing optimization has long been a problem in the networking field. With the rapid development of user applications, network traffic is continuously increasing in dynamicity, making optimization of the routing problem NP-hard. Traditional routing algorithms cannot ensure both accuracy and efficiency. Deep reinforcement learning (DRL) has recently shown great potential in solving networking problems. However, existing DRL-based routing solutions cannot process the graph-like information in the network topology and do not generalize well when the topology changes. In this paper, we propose AutoGNN, which combines a GNN and DRL for the automatic generation of routing policies. In AutoGNN, the traffic distribution in the network topology is processed by a GNN, while a DRL framework is used to train the parameters of neural networks without human expertise. Our experimental results show that AutoGNN can improve the average end-to-end delay of the network by up to 19.7% as well as present more robustness against topology changes.

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Network Traffic Classification using Machine Learning Techniques over Software Defined Networks

Cited by 28 publications

References 19 publications

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

A Machine Learning-Based Distributed Denial of Service Detection Approach for Early Warning in Internet Exchange Points

Architecting a machine learning pipeline for online traffic classification in software defined networking using spark

An Approach to Combine the Power of Deep Reinforcement Learning with a Graph Neural Network for Routing Optimization

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