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Many current-generation P2P applications use ephemeral ports, and in some cases, use ports of well-known services such as Web and FTP to make them indistinguishable to the port-based classifier.
Techniques that rely on inspection of packet contents have been proposed to address the diminished effectiveness of port-based classification. These approaches attempt to determine whether or not a flow contains a characteristic signature of a known application. Studies show that these approaches work very well for today’s Internet traffic, including P2P flows. In fact, commercial bandwidth management tools and network security appliances use application signature matching to enhance robustness of classification and deep inspection of packet content even in the case of encapsulated protocols within each other (i.e., x in HTTP). Indeed, very recently several threats appeared to use this technique to hide their presence and break through firewalls and other security devices. The progress in hardware acceleration has allowed packet content inspection techniques to run at speeds as high as 40 Gbps and made them the most commonly used approach to gain visibility into any Internet stream.
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Unfortunately, only a few service providers today have equipped their networks with packet inspection appliances |
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coarser information is provided (i.e., traffic flows). Unfortunately, only a few service providers today have equipped their networks with packet inspection appliances, while the majority of them has access only to traffic flows extracted directly from the routers, either sampled or unsampled.
To overcome these two fundamental problems of packet content inspection appliances, the research community has focused on a new family of techniques called “flow-features-based analysis.” The common goal of these techniques is to identify which application class a traffic flow belongs to when using traffic flow information only. These techniques achieve the flow-application class mapping by extracting and analyzing hidden properties of the flow, either in terms of “social interaction” of hosts engaged in such a flow or the spatial-temporal behavior of several flow features such as flow duration, number and size of packets per flow, inter-packet arrival time, and so on. A
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Nevertheless, packet-inspection approaches face two severe limitations. First, these techniques only identify traffic for which signatures are available. Maintaining an up-to-date list of signatures is a daunting task. Information is rarely available, up to date or complete. Furthermore, the traditional ad-hoc growth of IP networks, the continuing rapid proliferation of applications of different kinds, and the relative ease with which almost any user can design and infiltrate a new application to the traffic mix in the network with no centralized registration, contribute to this “knowledge gap.” Second, packet inspection techniques only work if full packets (i.e., header and payload) are available as an input and are completely harmless whenever
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variety of more sophisticated data mining algorithms have been proposed on top of such framework, such as supervised and un-supervised machine learning, clustering and graph-theoretical approaches, to increase the detection rate while decreasing the false-positive rate.
These techniques all lack a fundamental attribute that make them impractical from an operational perspective (i.e., the precision identification of the application responsible for the observed flow in contrast to packet content inspection techniques). This is a fundamental question to answer, as today’s network operators must know the nature --
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