English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

With the rapid development of Green Communication Network, the types andquantity of network traffic data are accordingly increasing. Network trafficclassification become a non-trivial research task in the area of networkmanagement and security, which not only help to improve the fine-grainednetwork resource allocation, but also enable policy-driven network management.Meanwhile, the combination of SDN and Edge Computing can leverage both SDN atits global visiability of network-wide and Edge Computing at its low latencyand good privacy-preserving. However, capturing large labeled datasets is acumbersome and time-consuming manual labor. Semi-Supervised learning is anappropriate technique to overcome this problem. With that in mind, we proposeda Generative Adversarial Network (GAN)-based Semi-Supervised Learning EncryptedTraffic Classification method called \emph{ByteSGAN} embedded in SDN EdgeGateway to achieve the goal of traffic classification in a fine-grained mannerto further improve network resource utilization. ByteSGAN can only use a smallnumber of labeled traffic samples and a large number of unlabeled samples toachieve a good performance of traffic classification by modifying the structureand loss function of the regular GAN discriminator network in a semi-supervisedlearning way. Based on public dataset 'ISCX2012 VPN-nonVPN', two experimentalresults show that the ByteSGAN can efficiently improve the performance oftraffic classifier and outperform the other supervised learning method likeCNN.

ByteSGAN: A Semi-supervised Generative Adversarial Network for Encrypted Traffic Classification of SDN Edge Gateway in Green Communication Network