Machine Learning

Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

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  • arXiv
    5 pts

    Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

    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 exploit 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 integrate both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both traffic categorization in which the network traffic is categorize into major classes (e.g. FTP, P2P, etc.) and application identification in which one identify end-user applications (e.g., BitTorrent, Skype, etc.). Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. After initial pre-processing phase on data, packets are fed to 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 $F_{1}$ score of $0.95$ in application identification and it also accomplished $F_{1}$ score of $0.97$ in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the classification and categorization methods on UNB ISCX VPN-nonVPN dataset.

    Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
    by Mohammad Lotfollahi, Ramin Shirali, Mahdi Jafari Siavoshani, Mohammdsadegh Saberian
    https://arxiv.org/pdf/1709.02656v1.pdf

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