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Li Teng, Qiao Wei, Zhang Jiawei, Gao Yiyang, Wang Shenao, Shen Yulong, Ma Jianfeng. Privacy-Preserving Network Attack Provenance Based on Graph Convolutional Neural Network[J]. Journal of Computer Research and Development, 2021, 58(5): 1006-1020. DOI: 10.7544/issn1000-1239.2021.20200942
Citation: Li Teng, Qiao Wei, Zhang Jiawei, Gao Yiyang, Wang Shenao, Shen Yulong, Ma Jianfeng. Privacy-Preserving Network Attack Provenance Based on Graph Convolutional Neural Network[J]. Journal of Computer Research and Development, 2021, 58(5): 1006-1020. DOI: 10.7544/issn1000-1239.2021.20200942

Privacy-Preserving Network Attack Provenance Based on Graph Convolutional Neural Network

Funds: This work was supported by the National Natural Science Foundation of China (61902291), the China Postdoctoral Science Foundation (2019M653567), the Natural Science Foundation of Shaanxi Province of China (2019JM-425), and the Fundamental Research Funds for the Central Universities (JB191507).
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  • Published Date: April 30, 2021
  • APT(advanced persistent threat) attacks have a long incubation time and a vital purpose. It can destroy the inside’s enterprise security fortress, employing variant Trojans, ransomware, and botnet. However, the existing attack source tracing methods only target a single log or traffic data, making it impossible to trace the complete process of multi-stage attacks. Because of the complicated log relationship, serious state explosion problems will occur in the log relationship graph, making it difficult to classify and identify attacks accurately. Simultaneously, data privacy protection is rarely considered in using log and traffic data for attack tracing approaches. We propose an attack tracing method based on a Graph Convolutional Network (GCN) with user data privacy protection to solve these problems. Supervised learning solves the state explosion caused by multiple log relationship connections, optimizing the Louvain community discovery algorithm to improve detection speed and accuracy. Moreover, using map neural networks to attack classification effectively and combining privacy protection scheme leveraging CP-ABE (Ciphertext-Policy Attribute Based Encryption) properties realize log data secure sharing in public cloud. In this paper, the detection speed and efficiency of four APT attack testing methods are reproduced. Experimental results show that the detection time of this method can be reduced by 90% at most, and the accuracy can reach 92%.
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