DocumentCode :
175394
Title :
Three Statistical Approaches to Sessionizing Network Flow Data
Author :
Rubin-Delanchy, Patrick ; Lawson, Daniel J. ; Turcotte, Melissa J. ; Heard, Nick ; Adams, Niall M.
Author_Institution :
Sch. of Math., Univ. of Bristol, Bristol, UK
fYear :
2014
fDate :
24-26 Sept. 2014
Firstpage :
244
Lastpage :
247
Abstract :
The network traffic generated by a computer, or a pair of computers, is often well modelled as a series of sessions. These are, roughly speaking, intervals of time during which a computer is engaging in the same, continued, activity. This article explores a variety of statistical approaches to re-discovering sessions from network flow data using timing alone. Solutions to this problem are essential for network monitoring and cyber-security. For example overlapping sessions on a computer network can be evidence of an intruder ´tunnelling´.
Keywords :
computer network management; computer network reliability; computer network security; computers; cyber security; intruder tunnelling; network flow data; network monitoring; network traffic; statistical approaches; Bayes methods; Computational modeling; Computers; Data models; Educational institutions; Mathematical model; Stochastic processes; clustering; network flow; point process; sessionization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics Conference (JISIC), 2014 IEEE Joint
Conference_Location :
The Hague
Print_ISBN :
978-1-4799-6363-8
Type :
conf
DOI :
10.1109/JISIC.2014.46
Filename :
6975583
Link To Document :
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