Abstract—Peer2Peer traffic already accounts for a large
share of the overall internet traffic. Future solutions will need to
manage all the available resources in order to charge users using
fair rules according to their communication profile. Obtaining
information about the behavior of Internet traffic is therefore
fundamental to the management, monitoring and operation
activities, such as the identification of applications and protocols
that customers use. However, the main obstacle to this
identification is the lack of scalability for monitoring network
devices. In particular, they can analyze all the network packets
for this purpose. This task is extremely demanding and almost
impossible to accomplish rapidly in large networks (because
usually there is a number in the hundreds or thousands of
customers). Furthermore, we expect such networks to become
even larger, as on the internet of things all devices (sensors,
appliances, etc) will be publicly connected to the internet. As
such, traffic sampling strategies have been proposed to
overcome this major problem of scale. This paper presents
different works in the area of monitoring traffic for user
profiling and security purposes. It proposes as well a solution
that uses selective filtering techniques combined with an engine
traffic DPI to identify applications and protocols that customers
use most frequently. Thus it becomes possible to get ISPs to
optimize their network in a scalable and intelligent manner,
imposing security measures in order to enforce network usage
according to client profiles.
Index Terms—Adaptive sampling, client profiling, deep
packet inspection, intelligent networks, selective filtering.
D. Teixeira is with Nokia Siemens Networks and Instituto Superior
Técnico (e-mail: diogo.teixeira.ext@nsn.com).
A. Arsénio is with the Instituto Superior Técnico (e-mail:
artur.arsenio@ist.utl.pt).
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Cite:Diogo Teixeira and Artur Arsénio, "Intelligent Network Client Profiler," Journal of Advances in Computer Networks vol. 1, no. 2, pp. 125-131, 2013.
