Abstract
Network intelligence has become an important trend in modern communication networks. In the future 6G network, the unrestricted communication between massive heterogeneous terminals will lead to more and more kinds of DDoS attacks, which will become an important factor affecting network security. In this paper, we propose a knowledge base detection scheme for malicious behavior of DDoS attacks based on graph neural networks. First, this paper constructs a malicious behavior knowledge base for a variety of common DDoS attacks. Considering the problem of multi-source heterogeneity under 6G network, this paper proposes a malicious behavior knowledge graph construction algorithm, which constructs a global malicious behavior knowledge graph from both address correlation and time correlation of network services. And the graph attention network is introduced on the basis of the knowledge graph to identify the malicious behaviors occurring in the network. The experimental results show that the detection scheme can enrich the feature representation of malicious behavior nodes. The scheme has a better performance compared with the machine learning scheme, and ultimately reduces the malicious traffic caused by DDoS attacks by more than an order of magnitude.
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This paper is supported by National Key R&D Program of China under Grant No. 2018YFA0701604.
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Liu, O., Li, K., Yin, Z., Zhou, H. (2023). A Graph Neural Network Detection Scheme for Malicious Behavior Knowledge Base. In: You, I., Kim, H., Angin, P. (eds) Mobile Internet Security. MobiSec 2022. Communications in Computer and Information Science, vol 1644. Springer, Singapore. https://doi.org/10.1007/978-981-99-4430-9_9
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DOI: https://doi.org/10.1007/978-981-99-4430-9_9
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