The research on Internet traffic classification and identification, with application on prevention of attacks and intrusions, increased considerably in the past years. Strategies based on statistical characteristics of the Internet traffic, that use parameters such as packet length (size) and inter-arrival time and their probability 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 beta distribution, according to the traffic generated by the users. The proposed functions has parameters that depend on the type of traffic and can be used as part of an Internet traffic classification and identification strategy. The models can be used to compare, simulate and estimate the computer network traffic, as well as to generate synthetic traffic and estimate the packets processing capacity of Internet routers
KEYWORDS Packet-switching networks, Measurement, Modeling, Statistical methods & Packet length.