Learning Approximate Execution Semantics From Traces for Binary Function Similarity-Reference-Cited by-同舟云学术

Learning Approximate Execution Semantics From Traces for Binary Function Similarity

Published:2023-04-01 Issue:4 Volume:49 Page:2776-2790
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Pei Kexin¹^ORCID,Xuan Zhou²,Yang Junfeng¹,Jana Suman¹,Ray Baishakhi¹^ORCID

Affiliation:

1. Columbia University, New York, NY, USA

2. Purdue University, West Lafayette, IN, USA

Funder

National Science Foundation

ONR

NSF CAREER

ARO Young Investigator

Google Faculty Fellowship

JP Morgan Faculty Research Award

DiDi Faculty Research Award

Google Cloud

Capital One Research

Amazon Web Services

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

https://ieeexplore.ieee.org/ielam/32/10103953/10002189-aam.pdf

Reference100 articles.

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3. Modeling functional similarity in source code with graph-based Siamese networks;mehrotra,2020

4. Software ethology: An accurate and resilient semantic binary analysis framework;mckee,2019

5. Semantics-based obfuscation-resilient binary code similarity comparison with applications to software plagiarism detection;luo;Proc Sixth ACM SIGSOFT Int'l Symp Foundations of Software Eng,2014

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