DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode-Reference-Cited by-同舟云学术

DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode

Published:2023-10-01 Issue:10 Volume:49 Page:4691-4706
ISSN:0098-5589
Container-title:IEEE Transactions on Software Engineering
language:
Short-container-title:IIEEE Trans. Software Eng.

Author:

Sun Tiezhu¹^ORCID,Allix Kevin¹^ORCID,Kim Kisub²^ORCID,Zhou Xin²^ORCID,Kim Dongsun³^ORCID,Lo David²^ORCID,Bissyandé Tegawendé F.¹^ORCID,Klein Jacques¹^ORCID

Affiliation:

1. University of Luxembourg, Kirchberg, Luxembourg

2. Singapore Management University, Singapore

3. Kyungpook National University, Daegu, Republic of Korea

Funder

Fonds National de la Recherche (FNR), Luxembourg

National Research Foundation of Korea

Korea government

National Cybersecurity Research and Development Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

http://xplorestaging.ieee.org/ielx7/32/10286436/10237047.pdf?arnumber=10237047

Reference93 articles.

1. A multi-view context-aware approach to Android malware detection and malicious code localization

2. Fine-grained Android Malware Detection based on Deep Learning

3. AndroAnalyzer: android malicious software detection based on deep learning

4. A Survey of Android Malware Detection with Deep Neural Models

5. CodeBERT: A Pre-Trained Model for Programming and Natural Languages

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