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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The machine learning algorithm is gaining prominence in trafficidentification research as it offers a way to overcome the shortcomings ofport-based and deep packet inspection, especially for P2P-based Skype.However,recent studies have focused mainly on traffic identification based on afull-packet dataset, which poses great challenges to identifying online networktraffic. This study aims to provide a new flow identification algorithm bytaking the sampled flow records as the object. The study constructs flowrecords from a Skype set as the dataset, considers the inherent NETFLOW andextended flow metrics as features, and uses a fast correlation-based filteralgorithm to select highly correlated features. The study also proposes a newNFI method that adopts a Bayesian updating mechanism to improve the classifiermodel. The experimental results show that the proposed scheme can achieve muchbetter identification performance than existing state-of-the-art trafficidentification methods, and a typical feature metric is analyzed in thesampling environment. The NFI method improves identification accuracy andreduces false positives and false negatives compared to other methods.

Online Encrypted Skype Identification Based on an Updating Mechanism