Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC)...

Full description

Saved in:

Bibliographic Details
Main Authors:	Muhammad Asam, Shaik Javeed Hussain, Mohammed Mohatram, Saddam Hussain Khan, Tauseef Jamal, Amad Zafar, Asifullah Khan, Muhammad Umair Ali, Umme Zahoora
Format:	article
Language:	EN
Published:	MDPI AG 2021
Subjects:	malware classification detection deep learning deep features convolutional neural networks transfer learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Online Access:	https://doaj.org/article/ea324eb47c8b4fd393023f7427e76be3
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

Similar Items