Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

Filus, Katarzyna; Domański, Adam; Domańska, Joanna; Marek, Dariusz; Szyguła, Jakub

doi:10.3390/e22101159

Cited by 3 publications

(6 citation statements)

References 40 publications

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“…After comparing the results obtained with different activation functions, the results have shown that the most efficient model used Sigmoid activation function in the output layer, therefore we chose this function for further experiments. The decisions made in this work were also influenced by our previous work regarding traffic classification in terms of the degree of self-similarity [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our previous works were also related to this topic. They regarded determining the degree of traffic self-similarity expressed by the Hurst parameter and also using data obtained from the IITiS data traffic traces to examine self-similar properties [ 25 ]. Self-similarity significantly impacts queue occupancy and transmission performance [ 47 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The conceptual structure of the model used in this paper is presented in Figure 1 . To design this model structure we relied on the experience of our earlier work [ 25 ], where the degree of self-similarity of network traffic was classified using Convolutional Neural Networks expressed by Hurst parameter.…”

Section: Data Preparation and Neural Network Training Processmentioning

confidence: 99%

“…The source of traffic was self-similar and based on Fractional Gaussian Noise (FGN) process. The advantages of such a source have been described previously in the articles [ 10 , 15 , 25 ].…”

Section: Data Preparation and Neural Network Training Processmentioning

confidence: 99%

“…In a different work, [ 24 ], CNNs were used to discover the network attacks, namely Distributed Denial of Service (DDoS) attacks. Additionally, our previous work [ 25 ] uses ANNs to examine the self-similar properties of the network traffic expressed by the Hurst parameter H . This approach also uses Convolutional Neural Networks.…”

Section: Introductionmentioning

confidence: 99%

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Data Preparation and Neural Network Training Processmentioning

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Personalized Recommendation of Rural Tourism Based on Traffic Classification and User Data Analysis

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Recently, With the development of global agricultural industrialization, modern agriculture has the functions of improving the quality of the ecological environment and providing people with the functions of sightseeing, leisure, and vacation. How to take the user as the center and combine the user’s personalized characteristics to offer the rural tourism products they need has become a research problem with real-world application value and challenge. In this context, this study develops an intelligent recommendation model by extensively analyzing the contents of rural tourism information platforms and product recommendation factors, as well as a rough set algorithm and traffic classification. To minimize the attributes of rural tourism product information and extract the core attribute, an attribute reduction approach based on a different matrix is implemented. Moreover, user interest similarity is computed and ranked to recommend rural tourist products. In addition, a personalized tourism attraction recommendation model is presented based on geographic area and period. The model achieved the highest average accuracy of 0.87%. The relevant experimental test results reveal that the system can provide accurate recommendations and services for rural tourism products.

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Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis

Cited by 3 publications

References 40 publications

Supervised Learning of Neural Networks for Active Queue Management in the Internet

Supervised Learning of Neural Networks for Active Queue Management in the Internet

Lightweight Deep Learning-Based Model for Traffic Prediction in Fog-Enabled Dense Deployed IoT Networks

Personalized Recommendation of Rural Tourism Based on Traffic Classification and User Data Analysis

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