| Title | Poster: Flexible Function Estimation of IoT Malware Using Graph Embedding Technique |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Oshio, Kei, Takada, Satoshi, Han, Chansu, Tanaka, Akira, Takeuchi, Jun'ichi |
| Conference Name | 2022 IEEE Symposium on Computers and Communications (ISCC) |
| Keywords | Computers, directed graphs, Estimation, graph embedding, graph theory, Human Behavior, IoT malware, Malware, malware analysis, Metrics, privacy, pubcrawl, resilience, Resiliency, Resiliency Coordinator, Signature Matching, source coding, static analysis |
| Abstract | 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. |
| DOI | 10.1109/ISCC55528.2022.9912475 |
| Citation Key | oshio_poster_2022 |
Poster: Flexible Function Estimation of IoT Malware Using Graph Embedding Technique