MalGNE: Enhancing the Performance and Efficiency of CFG-Based Malware Detector by Graph Node Embedding in Low Dimension Space-Reference-Cited by-同舟云学术

MalGNE: Enhancing the Performance and Efficiency of CFG-Based Malware Detector by Graph Node Embedding in Low Dimension Space

Published:2024 Issue: Volume:19 Page:4881-4896
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Peng Hao¹,Yang Jieshuai²,Zhao Dandan²^ORCID,Xu Xiaogang²^ORCID,Pu Yuwen³^ORCID,Han Jianmin²,Yang Xing⁴^ORCID,Zhong Ming²^ORCID,Ji Shouling³^ORCID

Affiliation:

1. College of Computer Science and Technology, and the Key Laboratory of Intelligent Educational Technology and Application, Zhejiang Normal University, Jinhua, China

2. College of Computer Science and Technology, Zhejiang Normal University, Jinhua, China

3. Institute of Cyberspace Research, and the College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China

4. State Key Laboratory of Pulsed Power Laser Technology, Electronic Countermeasure Institute, National University of Defense Technology, Hefei, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Zhejiang Province Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10500474.pdf?arnumber=10500474

Reference60 articles.

1. A Hierarchical Graph-Based Neural Network for Malware Classification

2. MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection

3. DLGraph: Malware Detection Using Deep Learning and Graph Embedding

4. Behavioral malware detection using deep graph convolutional neural networks;Oliveira;Authorea Preprints,2023

5. Spectral-Based Directed Graph Network for Malware Detection