Statistical traffic classification by boosting support vector machines

Sena, Gabriel Gómez; Belzarena, Pablo

doi:10.1145/2382016.2382019

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…Adaboost is a boosting algorithm that works on the weights of incorrectly classified instances; the classifier

tries to adjust the weights resulting from the classifier n such that subsequent classifiers will focus on more difficult cases. These techniques have been used in many applications, such as traffic classification [ 39 ]. The estimator used in our practical case study is SVM, the regularization factor was set to

and the kernel used was radial basis functions (RBF) .…”

Section: Proposed Approach Materials and Methodsmentioning

confidence: 99%

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Rubio‐Loyola

Paul-Fils

2022

Sensors

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Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.

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“…Adaboost is a boosting algorithm that works on the weights of incorrectly classified instances; the classifier

and the kernel used was radial basis functions (RBF) .…”

Section: Proposed Approach Materials and Methodsmentioning

confidence: 99%

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Rubio‐Loyola

Paul-Fils

2022

Sensors

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“…Among them, α i is the Lagrange multiplier, and the corresponding training sample point is the support vector when α i is not 0. K is a kernel function to solve nonlinear problems . C is a penalty coefficient, which is used to control the error during the training.…”

Section: Proposed Methods Of Spp‐svmmentioning

confidence: 99%

“…K is a kernel function to solve nonlinear problems. 32,33 C is a penalty coefficient, which is used to control the error during the training.…”

Section: Two-class Svmmentioning

confidence: 99%

An accurate traffic classification model based on support vector machines

Cao

Fang

et al. 2017

Int. J. Network Mgmt

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SummaryNetwork traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two-and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two-and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.

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“…The filter-wrapper feature selection method selects the key feature combinations accurately and avoids the false deletion of combined features, which achieves the effect of reducing the dimension and shortening the training time. The parameter optimization is used in reference [21,33], and it is the original grid search method which brings a higher risk of overfitting. The back-propagation algorithm in [17] is a kind of local search algorithm, which may fall into a local optimum solution.…”

Section: Comparison Of Igs and Other Related Approachesmentioning

confidence: 99%

“…IGS_filter-wrapper_SVM Filter-Wrapper IGS more than 99.34% Ensemble learning [15] Identification Engineer no more than 99% Ensemble learning [16] Burst Threshold no more than 80% Deep learning [17] No Back-Propagation more than 80% SVM [21] Sequential forward Grid-Search 97.17% SVM [33] No Grid-Search more than 95%…”

Section: Feature Selection Parameters Optimization Classification Accmentioning

confidence: 99%

An Improved Network Traffic Classification Model Based on a Support Vector Machine

Cao

et al. 2020

Symmetry

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Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.

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Statistical traffic classification by boosting support vector machines

Cited by 6 publications

References 24 publications

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

An accurate traffic classification model based on support vector machines

An Improved Network Traffic Classification Model Based on a Support Vector Machine

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