On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification

Cherif, Iyad Lahsen; Kortebi, Abdesselem

doi:10.1109/wd.2019.8734193

Cited by 68 publications

(47 citation statements)

References 15 publications

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“…Using Machine learning methods, many solutions have been proposed for network traffic classification. In this context, Cherif et al [6] used the symmetric uncertainty feature selection method then XGBoost for traffic classification. Peng et al [7] tested ten well-known classifiers includes Adaboost, DT, RF, Naive Bayes classifiers.…”

Section: Related Workmentioning

confidence: 99%

Decision tree-based blending method using deep-learning for network management

Aouedi

Piamrat

Parrein

2022

NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium

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Network traffic classification is a key component for network management, Quality-of-Service management, as well as for network security. Therefore, developing machine learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. However, among the classification methods applied to network traffic classification so far, there is no one method that outperforms all the others. They all have advantages and inconveniences depending on the application domain. Therefore,this paper proposes an intelligent traffic management model by using deep learning (DL) that incorporates multiple Decision Treebased models. Our model deploys a blending ensemble learning method to combine tree-based classifiers in order to maximize generalization accuracy. Using two datasets, we show that our proposed ensemble model is efficient for network traffic classification. Furthermore, the proposed approach is also compared against other representative machine-learning and deep-learning models and the results demonstrate that our approach provides better performance compared to others.

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Section: Related Workmentioning

confidence: 99%

Decision tree-based blending method using deep-learning for network management

Aouedi

Piamrat

Parrein

2022

NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium

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“…To this end, ML-based traffic classification has been applied extensively against cyber attacks by diagnosing malicious traffic [10]- [14] and classifying encrypted traffic [15]- [19]. ML algorithms have also been used for comprehending the traffic flow [20], providing application-aware traffic classification [21], and classifying the network traffic via semi-supervised learning [15] or supervised learning [22] methods. It has also been suggested in [23] that using specially-collected network datasets, one could train supervised ML to identify different root causes for problems based on different raw performance monitoring patterns.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Network Status Detection and Fault Localization

Mohammed

Côté

et al. 2021

IEEE Trans. Instrum. Meas.

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Although the autonomous detection of network status and localization of network faults can be a valuable tool for network and service operators, very few works have investigated this subject. As a result in today's networks, fault detection and localization remains a mostly-manual process. In this paper, we propose a Machine Learning (ML) method that can automatically detect the status of a network and localize faults. Our method uses Decision Tree, Gradient Boosting (GB), and XGBoost (XGB) ML algorithms to detect the network status as Normal, Congestion, and Network Fault. In comparison, existing related work can at best classify the network status as faulty or non-faulty. Experimental results show that our method yields accuracies of up to 99% on a dataset collected through an emulated network.

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“…In [7], the authors have used two common machine learning algorithms, which are C4.5 and KNN (K-nearest neighbor), for traffic classification. Also, the authors in [19] used the Symmetric uncertainty feature selection method then XGBoost for traffic classification.…”

Section: Ml-based Traffic Classificationmentioning

confidence: 99%

A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification

Aouedi

Piamrat

Bagadthey

2020

2020 IEEE 28th International Conference on Network Protocols (ICNP)

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Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by denoising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.

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On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification

Cited by 68 publications

References 15 publications

Decision tree-based blending method using deep-learning for network management

Decision tree-based blending method using deep-learning for network management

Machine Learning-Based Network Status Detection and Fault Localization

A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification

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