Poster: Flexible Function Estimation of IoT Malware Using Graph Embedding Technique

被引：0

作者：

Oshio, Kei ^{[1
]}

Takada, Satoshi ^{[1
]}

Han, Chansu ^{[2
]}

Tanaka, Akira ^{[2
]}

Takeuchi, Jun'ichi ^{[1
]}

机构：

[1] Kyushu Univ, Fukuoka, Japan

[2] Natl Inst Informat & Commun Technol, Tokyo, Japan

来源：

2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022) | 2022年

关键词：

IoT malware; malware analysis; signature matching; graph embedding;

D O I：

10.1109/ISCC55528.2022.9912475

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Most IoT malware is variants generated by editing and reusing parts of the functions based on publicly available source codes. In our previous study, we proposed a method to estimate the functions of a specimen using the Function Call Sequence Graph (FCSG), which is a directed graph of execution sequence of function calls. In the FCSG-based method, the subgraph corresponding to a malware functionality is manually created and called a signature-FSCG. The specimens with the signature-FSCG are expected to have the corresponding functionality. However, this method cannot detect the specimens with a slightly different subgraph from the signature-FSCG. This paper found that these specimens were supposed to have the same functionality for a signature-FSCG. These specimens need more flexible signature matching, and we propose a graph embedding technique to realize it.

引用

页数：3

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