A Traffic Classification Method Based on Wavelet Spectrum of Scatter Factor and Improved K-means

Abstract

Based on the problem that supervised machine learning requires labeled samples and fails to identify unknown traffic, the author innovatively integrates wavelet transform and K-means algorithm of unsupervised machine learning by combining the advantage of wavelet transform in solving multi-fractal network traffic and proposes a traffic identification method based on wavelet spectrum of scatter factor and improved K-means. This method represents each stream sequence with wavelet spectrum of scatter factor, which is taken as the input of clustering algorithm. The author carries out a cluster analysis with GA K-means algorithm. The experimental result suggests that this method has an obvious superiority in stability and accuracy of classification.