Biblio

The challenge of network traffic classification exists at the heart of many networking related tasks aimed at improving the overall user experience and usability of the internet. Current techniques, such as deep packet inspection, depend heavily on interaction by network administrators and engineers to maintain up to date stores of application network signatures and the infrastructure required to utilize them effectively. In this paper, we introduce Network Traffic Images, a 2-dimensional (2D) formulation of a stream of packet header lengths, which enable us to employ deep convolutional neural networks for network traffic classification. Five different network traffic image orientation mappings are carefully designed to deduce the best way to transform the 1-dimensional packet-subflow into a 2D image. Two different mapping strategies, one packet-relative and the other time-relative, are experimented with to map the packets of a packet flow to the pixels in the image. Experiments shows that high classification accuracy can be achieved with minimal manual effort using network traffic images in deep learning.