A novel semi-supervised learning method for Internet application identification

Internet traffic classification aims to identify the kind of Internet traffic. With the rise of traffic encryption and multi-layer data encapsulation, some classic classification methods have lost their strength. In an attempt to increase classification performance, Machine Learning (ML) strategies have gained the scientific community interest and have shown themselves promising in the future of traffic classification, mainly in the recognition of encrypted traffic. However, some of these methods have a high computational resource consumption, which make them unfeasible for classification of large traffic flows or in real-time. Methods using statistical analysis have been used to classify real-time traffic or large traffic flows, where the main objective is to find statistical differences among flows or find a pattern in traffic characteristics through statistical properties that allow traffic classification. The purpose of this work is to address statistical methods to classify Internet traffic that were little or unexplored in the literature. This work is not generally focused on discussing statistical methodology. It focuses on discussing statistical tools applied to Internet traffic classification Thus, we provide an overview on statistical distances and divergences previously used or with potential to be used in the classification of Internet traffic. Then, we review previous works about Internet traffic classification using statistical methods, namely Euclidean, Bhattacharyya, and Hellinger distances, Jensen-Shannon and Kullback-Leibler (KL) divergences, Support Vector Machines (SVM), Correlation Information (Pearson Correlation), Kolmogorov-Smirnov and Chi-Square tests, and Entropy. We also discuss some open issues and future research directions on Internet traffic classification using statistical methods.

show abstract

“…Another challenge during validation is to collect original data in real time to obtain the ground truth [60].…”

Section: Validationmentioning

confidence: 99%

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The Internet is the greatest invention of the twenty-first century and has brought a lot of convenience to people's daily lives, and college students, as the main participants of the Internet, use its features to participate in online learning, communication, and shopping [1][2][3]. For this reason, the emergence of the Internet does not only facilitate people's lives but its benefits can also be reflected in the field of education [4].…”

Section: Introductionmentioning

confidence: 99%

The Origin and Significance of Research on Music Education Reform and Talent Cultivation Based on Evaluation Index

Zhou,

Huang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In order to explore the origin and significance of music education reform and talent cultivation in the new era, this paper constructs a new music curriculum evaluation model based on SVM. Firstly, we explore the path of music education reform and the goal of music talent cultivation by taking the trend of Internet of everything as the origin of the proposed “high-quality courses” for music in universities. Then, we analyzed that the recognition accuracy of SVM is significantly better than most machine learning methods in solving high-dimensional small sample data, so we established a new music curriculum teaching evaluation model on this basis. The average score of each index of the new course teaching effect is 74, and the evaluation grade is better, obtained by establishing the evaluation system of the new music course of SVM. In the evaluation index, the CVI indexes were all kept in the range of 0.82 to 0.98, which was higher than the standard 0.78 of the CVI index, and the Kappa value of each evaluation index was higher than 0.86. The attendance rate in college teaching rose from 78% to 100%, the ability to master music knowledge rose from 67% to 93%, the ability to apply knowledge rose from 76% to 92%, and the average score on weekly tests rose from 86 to 96. Finally, it is concluded that the Internet-based music education reform approach can promote the development of music education careers and cultivate the important means of all-around talents.

show abstract

“…Tis hardware is more advantageous in terms of monitoring efects and cost-efciency [22]. Common multispectral information processing methods include neural networks (NN), support vector machines (SVM), decision trees (DT), and other simple feature extraction methods, all of which can achieve relatively good target ftting accuracy [23][24][25]. Previous experimental processes with chlorophyll, leaf area, and nitrogen changes as research objectives had long data acquisition intervals, with relatively great changes in crop growth states.…”

Section: Introductionmentioning

confidence: 99%

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data

Cao

Liu

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations. Rapid and accurate monitoring of wheat growth in hilly areas is critical for determining plant protection operations and strategies. Currently, the operation time for FHB prevention and plant protection is primarily determined by manual tour inspection of plant growth, which has the disadvantages of low information gathering and subjectivity. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to collect wheat canopy multispectral images and heading rate information during the heading and flowering stages in order to develop a method for detecting the appropriate time for preventive control of FHB. A 1D convolutional neural network + decision tree model (1D CNN + DT) was designed. All the multispectral information was input into the model for feature extraction and result regression. The regression revealed that the coefficient of determination (R2) between multispectral information in the wheat canopy and the heading rate was 0.95, and the root mean square error of prediction (RMSE) was 0.24. This result was superior to that obtained by directly inputting multispectral data into neural networks (NN) or by inputting multispectral data into NN via traditional VI calculation, support vector machines regression (SVR), or decision tree (DT). On the basis of FHB prevention and control production guidelines and field research results, a discrimination model for FHB prevention and plant protection operation time was developed. After the output values of the regression model were input into the discrimination model, a 97.50% precision was obtained. The method proposed in this study can efficiently monitor the growth status of wheat during the heading and flowering stages and provide crop growth information for determining the timing and strategy of FHB prevention and plant protection operations.

show abstract

A novel semi-supervised learning method for Internet application identification

Cited by 9 publications

References 42 publications

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

The Origin and Significance of Research on Music Education Reform and Talent Cultivation Based on Evaluation Index

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data

Contact Info

Product

Resources

About