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8 December 2022 Research on family classification based on graph similarity
Zemin Guo, Xiaojian Liu
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Proceedings Volume 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022); 1247420 (2022) https://doi.org/10.1117/12.2653827
Event: Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 2022, Guilin, China
Abstract
With the continuous development of mobile devices, the rapid increase in the number of Android malware poses a huge threat to malware detection systems. By classifying malware samples into families, the features shared by malware in the same family can be utilized in the malware detection method, to achieve the effect of improving the detection rate of malware. In this paper, a family classification method based on graph similarity is proposed, which constructs a family matrix and a weight matrix for malicious families and performs family classification by calculating the similarity between the software and each family. Experiments show that the classification accuracy rate of this method for the Kmin family, Inconosys family, Ginimi family, and DroidKungFu family in the Drebin dataset is over 90%.
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Zemin Guo and Xiaojian Liu "Research on family classification based on graph similarity", Proc. SPIE 12474, Second International Symposium on Computer Technology and Information Science (ISCTIS 2022), 1247420 (8 December 2022); https://doi.org/10.1117/12.2653827
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KEYWORDS
Feature extraction
Machine learning
Mobile devices
Network security
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