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Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm...
As an important network management task, Internet traffic classification requires high throughput. Virtualization is a technique sharing the same piece of hardware for multiple users. We present a high-throughput and virtualized architecture for online traffic classification. To explore massive parallelism, we provide a conversion from a decision-tree into a compact rule set table, we employ modular...
Traffic classification is used to perform important network management tasks such as flow prioritization and traffic shaping/pricing. Machine learning techniques such as the C4.5 algorithm can be used to perform traffic classification with very high levels of accuracy; however, realizing high-performance online traffic classification engine is still challenging. In this paper, we propose a high-throughput...
Traffic classification is an essential task in network management. Recently, there has been a new trend in exploring Graphics Processing Unit (GPU) for network applications. These applications typically do not perform floating point operations and obtaining speedup can be challenging. In this paper, we design a high-performance traffic classifier based on an alternate representation of the C4.5 decision-tree...
Traffic classification is one of the kernel applications in network management. Many Machine Learning (ML) traffic classification algorithms are based on decision-trees. While most of the existing implementations of decision-trees are hardwarebased, a new trend in network applications is to use softwarebased solutions. The decision-tree used for traffic classification is highly unbalanced, it is challenging...
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