Game Traffic Classification Using Statistical Characteristics at the Transport Layer

Han, Young-Tae; Park, Hong-Shik

doi:10.4218/etrij.10.0109.0236

Cited by 16 publications

(6 citation statements)

References 21 publications

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“…Educational games can be improved with decision trees used for the identification of factors affecting user behavior and knowledge acquisition within educational online games [68]. In other applications, decision trees are used for Internet game addiction in adolescents [69] and game-traffic analysis at the transport layer [70].…”

Section: Classification Methods Used For Product-lifespan Predictionmentioning

confidence: 99%

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

et al. 2019

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One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.

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Section: Classification Methods Used For Product-lifespan Predictionmentioning

confidence: 99%

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

et al. 2019

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“…In addition, a lot of QoS related characteristics show locality to corresponding transform domain based on the above analysis results and [41]. According to [42], [43], locality can bring sparsity, which inspires that we can utilizes locality to define new QoS categories and sparsity to classify QoS categories for typical multimedia traffics.…”

Section: Dataset Preparationmentioning

confidence: 99%

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

et al. 2017

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To address the issue of fine-grained classification of Internet multimedia traffic from a Quality of Service (QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition (K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bag-QoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoS-words, Locality Constrained Feature Coding (LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines (SVM) classifier. Our experimental results demonstrate the feasibility of the proposed classification method.

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“…In this situation, the network manager must be able to Figure out the specific application of traffic for effective network management. Classification method with statistic signature [1] is one of the methods that detects and classifies specific applications.…”

Section: Introductionmentioning

confidence: 99%

Packet out-of-order and retransmission in statistics-based traffic analysis

Lee

Ahn

Kim

2014

The 16th Asia-Pacific Network Operations and Management Symposium

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With the rapid growth of the Internet, the importance of application traffic analysis increases for efficient network management. The statistical information in traffic flows, can be efficiently utilized for application traffic identification. However, the packet out-of-order and retransmission generated at the traffic collection point reduce the performance of the statistics-based traffic analysis. In this paper, we propose a novel method to detect and resolve the packet out-of-order and retransmission problem in order to improve completeness and accuracy of the traffic identification. To prove the feasibility of the proposed method, we applied our method to a real traffic analysis system using statistical flow information, and compared the performance of the system with the selected 9 popular applications. The experiment showed maximum 4.9% of completeness growth in traffic bytes, which shows that the proposed method contributes to the analysis of heavy flow.

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Game Traffic Classification Using Statistical Characteristics at the Transport Layer

Cited by 16 publications

References 21 publications

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

From the Hands of an Early Adopter’s Avatar to Virtual Junkyards: Analysis of Virtual Goods’ Lifetime Survival

Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

Packet out-of-order and retransmission in statistics-based traffic analysis

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