Cargando…
A Kullback-Liebler divergence-based representation algorithm for malware detection
BACKGROUND: Malware, malicious software, is the major security concern of the digital realm. Conventional cyber-security solutions are challenged by sophisticated malicious behaviors. Currently, an overlap between malicious and legitimate behaviors causes more difficulties in characterizing those be...
Autores principales: | Aboaoja, Faitouri A., Zainal, Anazida, Ghaleb, Fuad A., Alghamdi, Norah Saleh, Saeed, Faisal, Alhuwayji, Husayn |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10557483/ https://www.ncbi.nlm.nih.gov/pubmed/37810364 http://dx.doi.org/10.7717/peerj-cs.1492 |
Ejemplares similares
-
Clone Track Identification using the Kullback-Liebler Distance
por: Needham, M
Publicado: (2008) -
Data augmentation based malware detection using convolutional neural networks
por: Catak, Ferhat Ozgur, et al.
Publicado: (2021) -
Artificial intelligence-driven malware detection framework for internet of things environment
por: Alsubai, Shtwai, et al.
Publicado: (2023) -
FG-Droid: Grouping based feature size reduction for Android malware detection
por: Arslan, Recep Sinan
Publicado: (2022) -
Deep learning based Sequential model for malware analysis using Windows exe API Calls
por: Catak, Ferhat Ozgur, et al.
Publicado: (2020)