Cargando…
Explainable Artificial Intelligence-Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine-Tuned CNN-Based Transfer Learning Model
Automated malware detection is a prominent issue in the world of network security because of the rising number and complexity of malware threats. It is time-consuming and resource intensive to manually analyze all malware files in an application using traditional malware detection methods. Polymorph...
Autores principales: | Naeem, Hamad, Alshammari, Bandar M., Ullah, Farhan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307336/ https://www.ncbi.nlm.nih.gov/pubmed/35875737 http://dx.doi.org/10.1155/2022/7671967 |
Ejemplares similares
-
IoT malware detection architecture using a novel channel boosted and squeezed CNN
por: Asam, Muhammad, et al.
Publicado: (2022) -
An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications
por: Anand, Ankita, et al.
Publicado: (2021) -
Transfer Learning for Image-Based Malware Detection for IoT
por: Panda, Pratyush, et al.
Publicado: (2023) -
IoT malware: An attribute-based taxonomy, detection mechanisms and challenges
por: Victor, Princy, et al.
Publicado: (2023) -
Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation
por: Ullah, Farhan, et al.
Publicado: (2022)