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In this paper, we study how to initialize the convolutional neural network (CNN) model for training on a small dataset. Specially, we try to extract discriminative filters from the pre-trained model for a target task. On the basis of relative entropy and linear reconstruction, two methods, Minimum Entropy Loss (MEL) and Minimum Reconstruction Error (MRE), are proposed. The CNN models initialized by...
The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world...
Traditional application identification based on port numbers has become increasingly inaccurate. A more accurate alternative is to inspect the application payloads of traffic flows. The main drawback of such method is that target applications must be manually analyzed beforehand. Another alternative is to exploit the distinctive statistical properties of traffic flows and apply machine learning techniques...
To provide user privacy, several anonymization techniques (e.g., pseudonyms applied to MAC addresses) have been proposed in 802.11 networks. However, recent research done by Pang et al. has demonstrated that pseudonyms are not adequate to protect user privacy. The key idea of Pang et al.'s method is to locate implicit identifiers (e.g., IP addresses and port numbers a user frequently visits), build...
Network traffic classification plays an important role in various network activities. Due to the ineffectiveness of traditional port-based and payload-based methods, recent works proposed using machine learning methods to classify flows based on statistical characteristics. In this study, we evaluate the effectiveness of machine learning techniques on the real-time traffic classification problem....
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