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

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)...

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Détails bibliographiques
Auteurs principaux: Muhammad Asam, Shaik Javeed Hussain, Mohammed Mohatram, Saddam Hussain Khan, Tauseef Jamal, Amad Zafar, Asifullah Khan, Muhammad Umair Ali, Umme Zahoora
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
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
Accès en ligne:https://doaj.org/article/ea324eb47c8b4fd393023f7427e76be3
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https://doaj.org/article/ea324eb47c8b4fd393023f7427e76be3

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