Network traffic classification plays an important role in network resource management and security. The application of encryption techniques and the rapid increase in the size of network traffic have placed higher demands on traffic classification. In this paper, we design multi-headed attention (MHA) and deep metric learning (DML) in our model for network traffic classification. In addition, MHA-DML also extracts more subtle and highly differentiated features through the improved triplet measurement loss. Experimental results demonstrate that the model achieves the best classification on all three publicly available web traffic datasets. The MHA-DML guarantees detection accuracy even when facing a classification task with many categories.
To address the problem that traditional network defense techniques are difficult to cope with scanning attacks launched by internal attackers, this paper proposes a Virtual Network Topology Deception Defense mechanism(VNTDD)based on the idea of deception defense. In order to solve the problem of lack of randomness and security of virtual network topology, we divide the virtual network topology into three categories: forwarding nodes, real nodes and virtual nodes, and analyze their deployment locations and numbers respectively to generate a random virtual network topology, and enables the deployment of virtual network topology on the underlying real network through traffic control mechanisms. Finally, through experimental analysis, the VNTDD deception defense mechanism proposed in this paper can effectively prolong the scanning process of the intranet by internal attackers.
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