Internet traffic classification has become more important with rapid growth of current Internet network and online applications. There have been numerous studies on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which end-user applications (e.g., BitTorrent and Skype) identification is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and convolution neural network in order to classify network traffic. Deep packet with CNN as its classification model achieved recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.

Abstract-We consider the problem of multicasting information from a source to a set of receivers over a network where intermediate network nodes perform randomized linear network coding operations on the source packets. We propose a channel model for the noncoherent network coding introduced by Koetter and Kschischang in [6], that captures the essence of such a network operation, and calculate the capacity as a function of network parameters. We prove that use of subspace coding is optimal, and show that, in some cases, the capacity-achieving distribution uses subspaces of several dimensions, where the employed dimensions depend on the packet length. This model and the results also allow us to give guidelines on when subspace coding is beneficial for the proposed model and by how much, in comparison to a coding vector approach, from a capacity viewpoint. We extend our results to the case of multiple source multicast that creates a virtual multiple access channel.

We examine the problem of multiple sources transmitting information to one or more receivers that require the information from all the sources, over a network where the network nodes perform randomized network coding. We consider the noncoherent case, where neither the sources nor the receivers have any knowledge of the intermediate nodes operations. We formulate a model for this problem, inspired from blockfading noncoherent MIMO communications. We prove, using information theoretic tools, that coding over subspaces is sufcient to achieve the capacity, and give bounds for the capacity. We then examine the associated combinatorial problem of code design. We extend the work by Koetter and Kschischang [3] to code constructions for the multisource case. Our constructions can also be viewed as coding for the noncoherent multiple-access nite-eld channel. 978-1-4244-2571-6/08/$25.00 ©2008 IEEE

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