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Graph Similarity Metric Using Graph Convolutional Network: Application to Malware Similarity Match

Bing-lin ZHAO, Fu-dong LIU, Zheng SHAN, Yi-hang CHEN, Jian LIU

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Summary :

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.8 pp.1581-1585
Publication Date
2019/08/01
Publicized
2019/05/20
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDL8259
Type of Manuscript
LETTER
Category
Information Network

Authors

Bing-lin ZHAO
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Fu-dong LIU
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Zheng SHAN
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Yi-hang CHEN
  State Key Laboratory of Mathematical Engineering and Advanced Computing
Jian LIU
  Nanjing University of Finance and Economics

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