Unsupervised traffic classification using flow statistical properties and IP packet payload

► We proposed a novel unsupervised approach for network traffic classification. ► We introduced a bag-of-words model to represent the content of traffic clusters. ► We applied the latent semantic analysis to aggregate similar traffic clusters. ► For training, we combined flow statistical properties and IP packet payload. ► For testing, only flow statistical properties were used for classification.