IFAttn: Binary code similarity analysis based on interpretable features with attention-Reference-Cited by-同舟云学术

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IFAttn: Binary code similarity analysis based on interpretable features with attention

Published:2022-09 Issue: Volume:120 Page:102804
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Jiang Shuai^ORCID,Fu Cai,Qian Yekui,He Shuai,Lv Jianqiang,Han Lansheng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference30 articles.

1. Ba, J. L., Kiros, J. R., Hinton, G. E., 2016. Layer normalization. 1607.06450.

2. BYTEWEIGHT: Learning to recognize functions in binary code;Bao,2014

3. Binslayer: Accurate comparison of binary executables;Bourquin,2013

4. Bingo: Cross-architecture cross-os binary search;Chandramohan,2016

5. Learning a similarity metric discriminatively, with application to face verification;Chopra,2005

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1. HAformer: Semantic fusion of hex machine code and assembly code for cross-architecture binary vulnerability detection;Computers & Security;2024-10

2. AttnCall: Refining Indirect Call Targets in Binaries with Attention;Lecture Notes in Computer Science;2024

3. SENSE: An unsupervised semantic learning model for cross-platform vulnerability search;Computers & Security;2023-12

4. Similarity Regression Of Functions In Different Compiled Forms With Neural Attentions On Dual Control-Flow Graphs;The Computer Journal;2023-10-11

5. Learning Approximate Execution Semantics From Traces for Binary Function Similarity;IEEE Transactions on Software Engineering;2023-04-01

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