Cargando…
Data augmentation based malware detection using convolutional neural networks
Due to advancements in malware competencies, cyber-attacks have been broadly observed in the digital world. Cyber-attacks can hit an organization hard by causing several damages such as data breach, financial loss, and reputation loss. Some of the most prominent examples of ransomware attacks in his...
Autores principales: | Catak, Ferhat Ozgur, Ahmed, Javed, Sahinbas, Kevser, Khand, Zahid Hussain |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924722/ https://www.ncbi.nlm.nih.gov/pubmed/33816996 http://dx.doi.org/10.7717/peerj-cs.346 |
Ejemplares similares
-
Deep learning based Sequential model for malware analysis using Windows exe API Calls
por: Catak, Ferhat Ozgur, et al.
Publicado: (2020) -
Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images
por: Sahinbas, Kevser, et al.
Publicado: (2021) -
Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers
por: Atacak, İsmail, et al.
Publicado: (2022) -
Artificial intelligence-driven malware detection framework for internet of things environment
por: Alsubai, Shtwai, et al.
Publicado: (2023) -
A Kullback-Liebler divergence-based representation algorithm for malware detection
por: Aboaoja, Faitouri A., et al.
Publicado: (2023)