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Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification — Recommend this title to your library

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The main purpose of a network scheduler is to classify differently processed packets. Today, myriads of different methods are used to attain the network classification. The simplest of these would be to correlate parts of data patterns with the popular protocols. A rather advanced method statistically analyzes the packet inter-arrival times, byte frequencies, as well as packet sizes in order. After the traffic flow classification has been done through a certain protocol, a preset policy is used for the traffic flow, including the other flows. This process is conducted in order to achieve a particular quality, i.e., quality of service. This application should be conducted at the exact point when traffic accesses the network. It should also be carried out in a manner that allows the traffic management to take place, isolating the individual flows and queue from the traffic. These individual flows and queue will be shaped differently as well. The next network traffic classification approaches [7,9,17] are considered the most reliable, as they involve a full analysis of the protocol. However, these approaches have certain disadvantages, the first being the encrypted and proprietary protocols. As they do not have a public description, they cannot be classified. Although the implementation of every single protocol possible in the network is a thorough approach, in reality, this is extremely difficult. A single-state tracking protocol might demand quite a lot of resources. Consequently, the method loses its meaning and becomes impractical and unattainable.

Chapter Contents:

• 3.1 Port-based classification
• 3.2 Deep packet inspection (signature-based classification)
• 3.3 Connection pattern-based classification
• 3.4 Statistics-based classification
• 3.4.1 Feature selection
• 3.4.2 Classification methods
• 3.4.3 Ensemble learning
• 3.5 Network traffic classification issues
• 3.5.1 Summarization
• 3.5.2 Privacy preserving
• 3.5.3 Discretization
• 3.5.4 Sampling
• 3.5.5 Ground truth
• 3.6 Conclusion

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