Fusing feature engineering and deep learning: A case study for malware classification

Machine learning has become an appealing signature-less approach to detect and classify malware because of its ability to generalize to never-before-seen samples and to handle large volumes of data. While traditional feature-based approaches rely on the manual design of hand-crafted features based o...

Full description

Bibliographic Details
Main Authors:	Gibert, D. (Author), Le, Q. (Author), Mateu, C. (Author), Planes, J. (Author)
Format:	Article
Language:	English
Published:	Elsevier Ltd 2022
Subjects:	Benchmarking Case-studies Classification (of information) Deep learning Feature engineerings Feature extraction Feature fusion Features extraction Features fusions Gradient boosting Large volumes Learning systems Machine learning Machine-learning Malware Malware classification Malware classifications Malwares Multilayer neural networks Network layers Textures
Online Access:	View Fulltext in Publisher

Internet

View Fulltext in Publisher

Fusing feature engineering and deep learning: A case study for malware classification

Internet

Similar Items