Abstract
Nowadays, traffic identification is becoming increasingly important in network security. But in practice, we often encounter unknown traffic, in which we do not know its specific type, and makes it very difficult to manage and maintain network security. The ability to divide the mixed unknown traffic into multiple clusters, each of which contains only one type as far as possible, is a key point to tackle this problem. In this paper, we propose a framework for unknown traffic identification based on neural networks and constraint information to improve the clustering purity. The framework consists of two main innovations: (1) It uses neural network methods to reduce the dimensionality and select features of network traffic. (2) It analyzes the constraint information of traffic and uses this information to guide the process of identification. To verify the effectiveness of the framework in this paper, we make contrast experiments on two real-world packet traces respectively. Through our experimental results, we find that the maximum clustering purity of our framework in this paper can reach 96.10% on the traces of Internet Service Provider (ISP) and 91.89% on the public traces. Experimental results show that the proposed framework is more effective than Gaussian Mixture Model (GMM).
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Kang, L., Ji, Q., Ni, L., Li, J. (2022). A Framework for Unknown Traffic Identification Based on Neural Networks and Constraint Information. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_7
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