2-SPIFF: a 2-stage packer identification method based on function call graph and file attributes

被引:0
作者
Hao Liu
Chun Guo
Yunhe Cui
Guowei Shen
Yuan Ping
机构
[1] Guizhou University,Guizhou Provincial Key Laboratory of Public Big Data, College of Computer Science and Technology
[2] Xuchang University,School of Information Engineering
来源
Applied Intelligence | 2021年 / 51卷
关键词
Packer identification; Function call graph; Feature extraction; Machine learning; Static analysis;
D O I
暂无
中图分类号
学科分类号
摘要
Most malware employs packing technology to escape detection; thus, packer identification has become increasingly important in malware detection. To improve the accuracy of packer identification, this article analyses the differences in the function call graph (FCG) and file attributes between the non-packed executable files and the executable files packed by different packers, and further proposes a 2-stage packer i dentification method based on FCG and file attributes (2-SPIFF). In 2-SPIFF, the detection model of stage I distinguishes non-packed executable files from packed executable files based on the graph features extracted from the FCG, while the identification model of stage II identifies the packer used for packing the original executable file by using the concatenated features extracted from the FCG and file attributes. The experimental results show that 2-SPIFF can achieve an accuracy of 99.80% for packer detection and an accuracy of 98.49% for packer identification.
引用
收藏
页码:9038 / 9053
页数:15
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