Traffic identification using artificial neural network [Internet traffic]

The paper investigates the use of artificial neural networks (ANN) to unconventionally classify Internet traffic. Structurally and functionally, the classifier used is a feedforward multilayer layer perceptron (FFMLP) network trained using backpropagation. The inputs are random samples of bits from a bit stream (i.e. all the inputs are either 1 or 0). The data was collected and pre-processed, then used to train, test and evaluate the classifier. Despite the lower capability to identify certain data types, the algorithm has shown that it has very good features as a classifier. SMTP, TELNET, FTP, HTTP, IP TELEPHONY and UDP data types were used in the investigation.