Internet video traffic classification using QoS features

Wang, Zaijian; Yang, Dong; Hai-xian, Shi; Yang, Lei; Tang, Peixiao

doi:10.1109/iccnc.2016.7440599

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Using silhouette analysis, they analyzed that their model successfully identifies the capture Video packets with an 86.5% and a score of 0.93 of Area under Curve (AUC). Researchers in [40] performed QoS based division of video traffic. They collected video samples on site of various video applications consisting of real-time interactive and streaming video.…”

Section: Videomentioning

confidence: 99%

Classification of Flow-based Network Applications using Fuzzy Logic

Khan,

Farooq

2023

Preprint

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Ever since the boom of social media, the internet has drastically changed our lives. From social mediaplatforms, Industry 4.0, Big Data, Cloud Computing to Augmented and Virtual reality, the amountof data on the internet has increased on an unprecedented scale, and it is not going to decrease in theyears to come. This increasing amount of data puts a huge strain on today’s network infrastructure. Itis the need of the hour to keep the internet infrastructure up-to-date to tackle the upcoming challenges.Internet Network Traffic Classification is thus a new paradigm in which lightweight sophisticatedalgorithms are designed and tested on live networks. This problem has been focused a lot with thehelp of Machine and Deep learning tools. These models do provide a high accuracy of result, but canbecome complex problems depending upon the specific application. In this paper, a Fuzzy Logic basedLightweight Classification Architecture is proposed and implemented to successfully identify Audioand Video from other Network Traffic (NT) applications. A detailed elaboration of features extractedfrom a variety of applications is presented. Fuzzy logic is used to distinguish among the classes basedon human intuition based rules. To verify the proposed algorithm, flow-based network captures isused to identify whether the correct class of network traffic is predicted or not. The results obtainedfrom the Fuzzy logic is also compared with traditional Logistic regression and kNN based MachineLearning algorithms to showcase the superiority of Fuzzy logic.

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Section: Videomentioning

confidence: 99%

Classification of Flow-based Network Applications using Fuzzy Logic

Khan,

Farooq

2023

Preprint

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“…Concerning classification of video traffic, several research applied the traditional machine learning (ML) methods in their approaches. Authors in [16] proposed uplink/downlink rate as traffic classification features. They adopted support vector machine as their classifier.…”

Section: Streamingmentioning

confidence: 99%

Deep Learning Approach for Real-time Video Streaming Traffic Classification

Jameel

Turner

Kanakis

et al. 2022

2022 International Conference on Computer Science and Software Engineering (CSASE)

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Video streaming services such as Amazon Prime Video, Netflix and YouTube, continue to be of enormous demands in everyday peoples' lives. This enticed research in new mechanisms to provide a clear image of network usage and ensure better Quality of Service (QoS) for these applications. This paper proposes an accurate video streaming traffic classification model based on deep learning (DL). We first collected a set of video traffic data from a real network. Then, data was pre-processed to select the desired features for video traffic classification. Based on the performance evaluation, the model produces an overall accuracy of 99.3% when classifying video streaming traffic using a multi-layer feedforward neural network. This paper also evaluates the DL approach's effectiveness compared to the Gaussian Naive Bayes algorithm (GNB), one of the most well-known machine learning techniques used in Internet traffic classification. The model is promising to be applied in a real-time scenario as it showed its ability to predict new unseen data with 98.4% overall accuracy.

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“…These approaches are very relevant to users and organizations requiring very specific types of traffic usage, and anomaly detection on their networks. The issue of classifying video traffic that traverses on the internet effectively with guaranteed Quality of Service is addressed by Zai-jian et al [26]. With one target application (Video Traffic) in focus, the authors use the QoS based Flow Aggregation framework to present a modified K-Singular Value Decomposition (K-SVD) framework.…”

Section: Literature Reviewmentioning

confidence: 99%

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

Takyi¹,

Bagga²,

Gupta³

2019

IJITEE

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The task of network administrators to identify and determine the type of traffic traversing through the network is very critical to the rapid growth of new traffic each day. As the requirements of networks change over time, the situation of the network not able to meet some requirements is likely to occur. In a wide area network with a limited resource such as the low speed of links, frequent fragmentation of packets leading to extreme packet loss and costs is prominent resulting in the poor quality of service. As a result quantified amount of traffic flows can be classified at a time with limited features lowering the effectiveness of traffic classification. To improve upon the classification in such scenarios, we propose a hybrid semi-supervised clustering that is able to classify packet flows with restricted features and a small amount of packets while maintaining high accuracy in classification. We implement the above scenario in simulation and classify the limited flows obtained with our proposed algorithm. Evaluation results show that our proposed algorithm implemented into a classifier has good accuracy and precision values, with low processing time and error rates. The proposed strategy will enable network administrators during times of network resource depletion or upgrades provide and ensure the best quality of services and identify unwanted or malicious traffic.

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Internet video traffic classification using QoS features

Cited by 6 publications

References 9 publications

Classification of Flow-based Network Applications using Fuzzy Logic

Classification of Flow-based Network Applications using Fuzzy Logic

Deep Learning Approach for Real-time Video Streaming Traffic Classification

A Semi-Supervised QoS-Aware Classification for Wide Area Networks with Limited Resources

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