Feature extraction based IP traffic classification using machine learning

With rapid growth in internet traffic over last couple of years due to the usage of large number of internet applications, IP traffic classification becomes very necessary for various internet service providers to optimize their network performance and for governmental intelligence organizations. Today, traditional IP traffic classification techniques such as port number and payload based direct packet inspection techniques are rarely used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques which inhibit inspection of packet payload. Current trends are use of machine learning (ML) techniques for IP traffic classification. In this research paper, two different real time internet traffic datasets has been developed using packet capturing tool for 2 minute and 2 second packet capturing duration. After that, five ML algorithms MLP, RBF, C4.5, Bayes Net and Naïve Bayes are employed for internet traffic classification with these datasets. This experimental analysis shows that Bayes Net and C4.5 are effective ML techniques for IP traffic classification with accuracy in the range of 88% with reduction in packet capturing duration.