A performance study of hidden markov model and random forest in internet traffic classification

Abstract

Currently, the emphasis on internet traffic classification is gaining momentum and a broad range of procedures has been developed for a variety of network tools. Machine learning methods are commonly utilized for the recognition and classification of internet traffic. This investigation trains its sights on two frequently employed techniques in this area. These are the Hidden Markov Model and the Random Forest procedure. Furthermore, the study offers a detailed explanation and analysis for the methodologies for each and forwards the advantages and setbacks attributed through comparing the performance in different significant factors.