Supervised Machine Learning based Classification of Video Traffic Types

Grabs, Elans; Pētersons, Ernests; Ipatovs, Aleksandrs; Chulkovs, Dmitrijs

doi:10.1109/ieeeconf49502.2020.9141625

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Researchers in [45] performed an experiment to distinguish between real-time and streaming videos. They used twitch platform to access both, real-time and stored (streamed) 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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“…Researchers in [45] performed an experiment to distinguish between real-time and streaming videos. They used twitch platform to access both, real-time and stored (streamed) video.…”

Section: Videomentioning

confidence: 99%