Towards Provable Network Traffic Measurement and Analysis via Semi-Labeled Trace Datasets

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Authors

ČERMÁK Milan JIRSÍK Tomáš VELAN Petr KOMÁRKOVÁ Jana ŠPAČEK Stanislav DRAŠAR Martin PLESNÍK Tomáš

Year of publication 2018
Type Article in Proceedings
Conference 2018 Network Traffic Measurement and Analysis Conference (TMA)
MU Faculty or unit

Institute of Computer Science

Citation
Web
Doi http://dx.doi.org/10.23919/TMA.2018.8506498
Keywords datasets;network traffic;analysis
Attached files
Description Research in network traffic measurement and analysis is a long-lasting field with growing interest from both scientists and the industry. However, even after so many years, results replication, criticism, and review are still rare. We face not only a lack of research standards, but also inaccessibility of appropriate datasets that can be used for methods development and evaluation. Therefore, a lot of potentially high-quality research cannot be verified and is not adopted by the industry or the community. The aim of this paper is to overcome this controversy with a unique solution based on a combination of distinct approaches proposed by other research works. Unlike these studies, we focus on the whole issue covering all areas of data anonymization, authenticity, recency, publicity, and their usage for research provability. We believe that these challenges can be solved by utilization of semi-labeled datasets composed of real-world network traffic and annotated units with interest-related packet traces only. In this paper, we outline the basic ideas of the methodology from unit trace collection and semi-labeled dataset creation to its usage for research evaluation. We strive for this proposal to start a discussion of the approach and help to overcome some of the challenges the research faces today.
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