Abstract:Decision tree (DT)-based machine learning (ML) algorithms are one of the preferred solutions for real-time internet traffic classification in terms of their easy implementation on hardware. However, the rapid increase in today’s newly developed applications and the resulting diversity in internet traffic greatly increases the size of DTs. Therefore, the tree-based hardware classifiers cannot keep up with this growth in terms of resource usage and classification speed. To alleviate the problem, we propose to gr… Show more
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