Practical evaluation of encrypted traffic classification based on a combined method of entropy estimation and neural networks

Zhou, Kun; Wang, Wenyong; Wu, Chenhuang; Hu, Teng

doi:10.4218/etrij.2019-0190

Cited by 28 publications

(8 citation statements)

References 21 publications

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“…Therefore, many scholars combine different evaluation methods to improve the defects of different evaluation methods. For example, Zhou et al (32) combined the entropy method with neural network in the evaluation; Li et al (33) combined the entropy method with TOPSIS; Zhong et al (34) combined the entropy method with the analytic hierarchy process; and Ming-Lang et al (35) used bibliometric analysis and fuzzy Delphi method. Because of the above analysis, this paper adopted the gray relational analysis method of entropy method.…”

Section: Methodsmentioning

confidence: 99%

Measurement of Agricultural Green Development Level in the Three Provinces of Northeast China Under the Background of Rural Vitalization Strategy

Hou

Wang

2022

Front. Public Health

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This paper offers an evaluation index system of agricultural green development level based on the Rural Vitalization Strategy. The evaluation index system includes four dimensions: agricultural development, ecological resource protection, environment-friendly, and industrial extension and integration. Then, the paper calculates the level of agricultural green development in the three provinces of Northeast China using the entropy-gray correlation method from 2009 to 2019. The calculation results show that between 2009 and 2019, the level of agricultural green development in the three northeastern provinces of China fluctuates and increases, and there is some variation between them: in terms of the overall level of agricultural green development Heilongjiang Province > Liaoning Province > Jilin Province, in terms of the average change speed of agricultural green development level, Heilongjiang Province > Liaoning Province > Jilin Province; in terms of each dimension, the average level of correlation degree of agricultural development dimension is Liaoning Province > Heilongjiang Province > Jilin Province, the average level of correlation degree of ecological resource protection dimension is Heilongjiang Province > Jilin Province > Liaoning Province, the average level of correlation degree of environment-friendly dimension is Heilongjiang Province > Jilin Province > Liaoning Province, and the average level of correlation degree of industrial extension and integration is Liaoning Province > Heilongjiang Province > Jilin Province. Finally, the basis of the calculation results, combined with the actual situation of agricultural development in the three provinces, the countermeasures, and suggestions for promoting the green development of agriculture are put forward. Specifically, it includes the establishment of an evaluation system for agricultural green development; strengthening the protection of agricultural ecological resources; increasing rural environmental governance; promoting the integrated development of rural tertiary industries; vigorously supporting agricultural scientific and technological innovation; and strengthening regional cooperation and other suggestions.

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Section: Methodsmentioning

confidence: 99%

Measurement of Agricultural Green Development Level in the Three Provinces of Northeast China Under the Background of Rural Vitalization Strategy

Hou

Wang

2022

Front. Public Health

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“…In [154], Wang et al used entropy to classify traffic more deeply. In [156], Zhou et al, used entropy for evaluation of encrypted traffic classification. In Table 12, there is a summary of works about the use of entropy.…”

Section: ) Shannon Entropymentioning

confidence: 99%

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

et al. 2022

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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.

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“…Flow-behavior-based approaches enable encrypted traffic to be classified by leveraging the behavior statistics. The authors of [8] introduced three representative encryption mechanisms of traffic and extracted the statistics from the encrypted traffic. Moreover, they evaluated the performance of several machine-learning algorithms such as a support vector machine, random forest, naive Bayes, logistic regression, and neural networks.…”

Section: Related Work a Flow Behavior-based Traffic Classificationmentioning

confidence: 99%

Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm

et al. 2021

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Traffic classification is widely used in various network functions such as software-defined networking and network intrusion detection systems. Many traffic classification methods have been proposed for classifying encrypted traffic by utilizing a deep learning model without inspecting the packet payload. However, they have an important challenge in that the mechanism of deep learning is inexplicable. A malfunction of the deep learning model may occur if the training dataset includes malicious or erroneous data. Explainable artificial intelligence (XAI) can give some insight for improving the deep learning model by explaining the cause of the malfunction. In this paper, we propose a method for explaining the working mechanism of deep-learning-based traffic classification as a method of XAI based on a genetic algorithm. We describe the mechanism of the deep-learning-based traffic classifier by quantifying the importance of each feature. In addition, we leverage the genetic algorithm to generate a feature selection mask that selects important features in the entire feature set. To demonstrate the proposed explanation method, we implemented a deep-learning-based traffic classifier with an accuracy of approximately 97.24%. In addition, we present the importance of each feature derived from the proposed explanation method by defining the dominance rate.

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Practical evaluation of encrypted traffic classification based on a combined method of entropy estimation and neural networks

Cited by 28 publications

References 21 publications

Measurement of Agricultural Green Development Level in the Three Provinces of Northeast China Under the Background of Rural Vitalization Strategy

Measurement of Agricultural Green Development Level in the Three Provinces of Northeast China Under the Background of Rural Vitalization Strategy

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

Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm

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