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CLC number: TP309.5
On-line Access: 2019-07-08
Received: 2018-08-31
Revision Accepted: 2019-01-05
Crosschecked: 2019-06-11
Cited: 0
Clicked: 5282
Malware homology identification based on a gene perspective
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Bing-lin Zhao, Zheng Shan, Fu-dong Liu, Bo Zhao, Yi-hang Chen, Wen-jie Sun. Malware homology identification based on a gene perspective[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 801-815.
@article{title="Malware homology identification based on a gene perspective",
author="Bing-lin Zhao, Zheng Shan, Fu-dong Liu, Bo Zhao, Yi-hang Chen, Wen-jie Sun",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="6",
pages="801-815",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800523"
}
%0 Journal Article
%T Malware homology identification based on a gene perspective
%A Bing-lin Zhao
%A Zheng Shan
%A Fu-dong Liu
%A Bo Zhao
%A Yi-hang Chen
%A Wen-jie Sun
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 6
%P 801-815
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800523
TY - JOUR
T1 - Malware homology identification based on a gene perspective
A1 - Bing-lin Zhao
A1 - Zheng Shan
A1 - Fu-dong Liu
A1 - Bo Zhao
A1 - Yi-hang Chen
A1 - Wen-jie Sun
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 6
SP - 801
EP - 815
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800523
Abstract: Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.
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