Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection

Ziyang Li; Hironori Washizaki; Yoshiaki Fukazawa

doi:10.1109/GCCE53005.2021.9621783

Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection

Ziyang Li, Hironori Washizaki, Yoshiaki Fukazawa

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	834-836
Number of pages	3
ISBN (Electronic)	9781665436762
DOIs	https://doi.org/10.1109/GCCE53005.2021.9621783
Publication status	Published - 2021
Event	10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan Duration: 2021 Oct 12 → 2021 Oct 15

Publication series

Name	2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference	10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/Territory	Japan
City	Kyoto
Period	21/10/12 → 21/10/15

Keywords

binary vulnerability detection
cross-architecture
intermediate language
similarity evaluation

ASJC Scopus subject areas

Computer Science Applications
Signal Processing
Biomedical Engineering
Electrical and Electronic Engineering
Media Technology
Instrumentation

Access to Document

10.1109/GCCE53005.2021.9621783

Cite this

Li, Z., Washizaki, H., & Fukazawa, Y. (2021). Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection. In 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021 (pp. 834-836). (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE53005.2021.9621783

Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection. / Li, Ziyang; Washizaki, Hironori ; Fukazawa, Yoshiaki.

2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 834-836 (2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, Z, Washizaki, H & Fukazawa, Y 2021, Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection. in 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021. 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 834-836, 10th IEEE Global Conference on Consumer Electronics, GCCE 2021, Kyoto, Japan, 21/10/12. https://doi.org/10.1109/GCCE53005.2021.9621783

@inproceedings{4dbc76e67e3346128375cdfc9f3267c1,

title = "Feature Extraction Method for Cross-Architecture Binary 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.",

keywords = "binary vulnerability detection, cross-architecture, intermediate language, similarity evaluation",

author = "Ziyang Li and Hironori Washizaki and Yoshiaki Fukazawa",

note = "Funding Information: The authors thank Toshiba members Yurie Fujimatsu and Jun Kanai for their helps during research collaboration. Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 ; Conference date: 12-10-2021 Through 15-10-2021",

year = "2021",

doi = "10.1109/GCCE53005.2021.9621783",

language = "English",

series = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "834--836",

booktitle = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",

}

TY - GEN

T1 - Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection

AU - Li, Ziyang

AU - Washizaki, Hironori

AU - Fukazawa, Yoshiaki

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - binary vulnerability detection

KW - cross-architecture

KW - intermediate language

KW - similarity evaluation

UR - http://www.scopus.com/inward/record.url?scp=85123454655&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85123454655&partnerID=8YFLogxK

U2 - 10.1109/GCCE53005.2021.9621783

DO - 10.1109/GCCE53005.2021.9621783

M3 - Conference contribution

AN - SCOPUS:85123454655

T3 - 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

SP - 834

EP - 836

BT - 2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Global Conference on Consumer Electronics, GCCE 2021

Y2 - 12 October 2021 through 15 October 2021

ER -

Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this