DocumentCode :
2487439
Title :
Using NoSQL databases for streaming network analysis
Author :
Wylie, Brian ; Dunlavy, Daniel ; Davis, Warren, IV ; Baumes, Jeff
fYear :
2012
fDate :
14-15 Oct. 2012
Firstpage :
121
Lastpage :
124
Abstract :
The high-volume, low-latency world of network traffic presents significant obstacles for complex analysis techniques. The unique challenge of adapting powerful but high-latency models to realtime network streams is the basis of our cyber security project. In this paper we discuss our use of NoSQL databases in a framework that enables the application of computationally expensive models against a real-time network data stream. We describe how this approach transforms the highly constrained (and sometimes arcane) world of real-time network analysis into a more developer friendly model that relaxes many of the traditional constraints associated with streaming data. Our primary use of the system is for conducting streaming text analysis and classification activities on a network link receiving ~200,000 emails per day.
Keywords :
SQL; computer network security; media streaming; pattern classification; telecommunication traffic; text analysis; NoSQL databases; classification activities; complex analysis techniques; cyber security project; network traffic; streaming network analysis; streaming text analysis; Analytical models; Data models; Databases; Electronic mail; Feature extraction; Pipelines; Real-time systems; NoSQL; analysis; database; email; informatics; network; real-time; streaming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Large Data Analysis and Visualization (LDAV), 2012 IEEE Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4673-4732-7
Type :
conf
DOI :
10.1109/LDAV.2012.6378986
Filename :
6378986
Link To Document :
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