BiTCN-TAEfficientNet malware classification approach based on sequence and RGB fusion

Xuan, Bona; Li, Jin; Song, Yafei

doi:10.1016/j.cose.2024.103734

Computers & Security

2024

DOI: 10.1016/j.cose.2024.103734

|View full text |Cite

BiTCN-TAEfficientNet malware classification approach based on sequence and RGB fusion

Bona Xuan,

Jin Li,

Yafei Song

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping

Li,

Wang,

Song

et al. 2024

Computers & Security

View full text Add to dashboard Cite

TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping

Li,

Wang,

Song

et al. 2024

Computers & Security

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

BiTCN-TAEfficientNet malware classification approach based on sequence and RGB fusion

Cited by 1 publication

References 15 publications

TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping

TriCh-LKRepNet: A large kernel convolutional malicious code classification network for structure reparameterisation and triple-channel mapping

Contact Info

Product

Resources

About