A2-CLM: Few-Shot Malware Detection Based on Adversarial Heterogeneous Graph Augmentation-Reference-Cited by-同舟云学术

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A2-CLM: Few-Shot Malware Detection Based on Adversarial Heterogeneous Graph Augmentation

Published:2024 Issue: Volume:19 Page:2023-2038
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Liu Chen¹^ORCID,Li Bo¹^ORCID,Zhao Jun²^ORCID,Feng Weiwei¹,Liu Xudong¹,Li Chunpei³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Beihang University, Beijing, China

2. School of Information Science and Engineering, Shandong Normal University, Jinan, China

3. School of Computer Science and Engineering and the School of Software, Guangxi Normal University, Guilin, China

Funder

Academic Excellence Foundation of Beihang University

Research on Soft and Hard Combination of Multi-Source Data Governance and Multi-Party Computation (MPC) Algorithm

Zhongguancun Laboratory

Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10368085.pdf?arnumber=10368085

Reference61 articles.

1. CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection

2. Unsuccessful story about few shot malware family classification and siamese network to the rescue

3. Dynamic Prototype Network based on Sample Adaptation for Few-Shot Malware Detection

4. A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

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