High Throughput and Programmable Online Traffic Classifier on FPGA
Machine Learning (ML) algorithms have been shown to be effective in classifying the dynamic internet traffic today. Using additional features and sophisticated ML techniques can improve accuracy and can classify a broad range of application classes. Realizing such classifiers to meet high data rates is challenging. In this paper, the authors propose two architectures to realize complete online traffic classifier using flow-level features. First, they develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL discretization algorithm. It achieves an accuracy of 97.92% when classifying a traffic trace consisting of eight application classes.