| Title | Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Li, Ziyang, Washizaki, Hironori, Fukazawa, Yoshiaki |
| Conference Name | 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) |
| Date Published | oct |
| Keywords | binary vulnerability detection, codes, compositionality, Conferences, Consumer electronics, cross-architecture, feature extraction, Human Behavior, Intermediate language, Metrics, pubcrawl, Resiliency, similarity evaluation, Software, Tools, vulnerability detection |
| Abstract | Vulnerability detection identifies defects in various commercial software. Because most vulnerability detection methods are based on the source code, they are not useful if the source code is unavailable. In this paper, we propose a binary vulnerability detection method and use our tool named BVD that extracts binary features with the help of an intermediate language and then detects the vulnerabilities using an embedding model. Sufficiently robust features allow the binaries compiled in cross-architecture to be compared. Consequently, a similarity evaluation provides more accurate results. |
| DOI | 10.1109/GCCE53005.2021.9621783 |
| Citation Key | li_feature_2021 |
Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection