Publication Details

Big Data Security Analysis with TARZAN Platform

RYCHLÝ Marek and RYŠAVÝ Ondřej. Big Data Security Analysis with TARZAN Platform. Journal of Cyber Security and Mobility, vol. 8, no. 2, 2018, pp. 165-188. ISSN 2245-1439. Available from: https://www.riverpublishers.com/journal.php?j=JCSM/8/2/jart
Czech title
Bezpečnostní analýza Big Data pomocí platformy TARZAN
Type
journal article
Language
english
Authors
URL
Keywords

Security, Big data, Framework

Abstract

The TARZAN platform is an integrated platform for analysis of digital data from security incidents. The platform serves primarily as a middleware between data sources and data processing applications, however, it also provides several supporting services and a runtime environment for the applications. The supporting services, such as a data storage, a resource and application registry, a synchronization service, and a distributed computing platform, are utilized by the TARZAN applications for various security-oriented analyses on the integrated data ranging from an IT security incident detection to inference analyses of data from social networks or crypto-currency transactions. To cope with a large amount of distributed data, both streamed in real-time and stored, and the need of a large scale distributed computing, the platform has been designed as a Big Data processing system ensuring reliable, scalable, and cost-effective solution. The platform is demonstrated on the case of a security analysis of network traffic.

Published
2018
Pages
165-188
Journal
Journal of Cyber Security and Mobility, vol. 8, no. 2, ISSN 2245-1439
Publisher
River Publishers
DOI
EID Scopus
BibTeX
@ARTICLE{FITPUB11558,
   author = "Marek Rychl\'{y} and Ond\v{r}ej Ry\v{s}av\'{y}",
   title = "Big Data Security Analysis with TARZAN Platform",
   pages = "165--188",
   journal = "Journal of Cyber Security and Mobility",
   volume = 8,
   number = 2,
   year = 2018,
   ISSN = "2245-1439",
   doi = "10.13052/jcsm2245-1439.822",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11558"
}
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