An information-theoretical approach to high-speed flow nature identification
IEEE/ACM Transactions on Networking (TON)
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The network community always pays its attention to find better methods for traffic classification, which is crucial for Internet Service Providers (ISPs) to provide better QoS for users. Prior works on traffic classification mainly focus their attentions on dividing Internet traffic into different categories based on application layer protocols (such as HTTP, Bit Torrent etc.). Making traffic classification from another point of view, we divide Internet traffic into different content types. Our technology is an attempt to solve the classification problem of network traffic, which contains unknown and proprietary protocols (i.e., no publicly available protocol specification). In this paper, we design a classifier which can distinguish Internet traffic into different content types using machine learning techniques. Features of our classifier are entropy of consecutive bytes and frequencies of characters. Our method is capable of classifying real-world traces into different content types (including Text, Picture, Audio, Video, Compressed, Base 64-encoded image, Base 64-encoded text and Encrypted). The chief features of our classifier are small computing space (about 1K Bytes) and high classification accuracy (about 81%).