Computer Software
Print ISSN : 0289-6540
Current issue
Showing 1-1 articles out of 1 articles from the selected issue
  • Yuuki TAKANO, Ryosuke MIURA, Shingo YASUDA, Kunio AKASHI, Tomoya INOUE
    2019 Volume 36 Issue 3 Pages 3_85-3_103
    Published: July 25, 2019
    Released: August 24, 2019

    Application-level network traffic analysis and sophisticated analysis techniques, such as machine learningand stream data processing for network traffic, require considerable computationalresources.In addition, developing an application protocol analyzer is a tediousand time-consuming task.Therefore, we propose a scalable and flexible traffic analysis platform (SF-TAP) for the efficientand flexible application-level streamanalysis of high-bandwidth network traffic.By using our flexible modular platform, developers can easilyimplement multicore scalable application-level stream analyzers.Furthermore, as SF-TAP is horizontally scalable, it manageshigh-bandwidth network traffic.To achieve this scalability, we separate the network trafficbased on traffic flows, and forward the separated flows to multipleSF-TAP cells, each comprising a traffic capturer andapplication-level analyzers.This study discusses the design, implementation and detailed evaluation of SF-TAP.

    Download PDF (1869K)