Author(s): Ewerton R. S. Castro | Marcelo S. Alencar | Iguatemi E. Fonseca
Journal: International Journal of Computer Networks & Communications
ISSN 0975-2293
Volume: 5;
Issue: 3;
Start page: 17;
Date: 2013;
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Keywords: Packet-switching networks | Measurement | Modeling | Statistical methods & Packet length
ABSTRACT
The research on Internet traffic classification and identification, with application on prevention of attacksand intrusions, increased considerably in the past years. Strategies based on statistical characteristics ofthe Internet traffic, that use parameters such as packet length (size) and inter-arrival time and theirprobability density functions, are popular. This paper presents a new statistical modeling for packet length,which shows that it can be modeled using a probability density function that involves a normal or a betadistribution, according to the traffic generated by the users. The proposed functions has parameters thatdepend on the type of traffic and can be used as part of an Internet traffic classification and identificationstrategy. The models can be used to compare, simulate and estimate the computer network traffic, as wellas to generate synthetic traffic and estimate the packets processing capacity of Internet router
Journal: International Journal of Computer Networks & Communications
ISSN 0975-2293
Volume: 5;
Issue: 3;
Start page: 17;
Date: 2013;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Packet-switching networks | Measurement | Modeling | Statistical methods & Packet length
ABSTRACT
The research on Internet traffic classification and identification, with application on prevention of attacksand intrusions, increased considerably in the past years. Strategies based on statistical characteristics ofthe Internet traffic, that use parameters such as packet length (size) and inter-arrival time and theirprobability density functions, are popular. This paper presents a new statistical modeling for packet length,which shows that it can be modeled using a probability density function that involves a normal or a betadistribution, according to the traffic generated by the users. The proposed functions has parameters thatdepend on the type of traffic and can be used as part of an Internet traffic classification and identificationstrategy. The models can be used to compare, simulate and estimate the computer network traffic, as wellas to generate synthetic traffic and estimate the packets processing capacity of Internet router
