From Data and Model Levels: Improve the Performance of Few-Shot Malware Classification-Reference-Cited by-同舟云学术

From Data and Model Levels: Improve the Performance of Few-Shot Malware Classification

Published:2022-12 Issue:4 Volume:19 Page:4248-4261
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Chai Yuhan¹^ORCID,Qiu Jing¹^ORCID,Yin Lihua¹,Zhang Lejun¹,Gupta Brij B.²^ORCID,Tian Zhihong¹^ORCID

Affiliation:

1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

2. Department of Computer Science and Information Engineering, International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan

Funder

Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme

Guangdong Higher Education Innovation Group

Guangdong Province Key Research and Development Plan

Major Key Project of PCL

National Key Research and Development Plan

National Natural Science Foundation of China

Guangzhou Higher Education Innovation Group

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10034788/09865222.pdf?arnumber=9865222

Reference74 articles.

1. Malware images

2. Malware Classification Using Attention-Based Transductive Learning Network

3. A mobile malware detection method using behavior features in network traffic

4. A few-shot meta-learning based siamese neural network using entropy features for ransomware classification

5. Byte-level malware classification based on markov images and deep learning

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