Cargando…
Attention-Based Automated Feature Extraction for Malware Analysis
Every day, hundreds of thousands of malicious files are created to exploit zero-day vulnerabilities. Existing pattern-based antivirus solutions face difficulties in coping with such a large number of new malicious files. To solve this problem, artificial intelligence (AI)-based malicious file detect...
Autores principales: | Choi, Sunoh, Bae, Jangseong, Lee, Changki, Kim, Youngsoo, Kim, Jonghyun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284474/ https://www.ncbi.nlm.nih.gov/pubmed/32443750 http://dx.doi.org/10.3390/s20102893 |
Ejemplares similares
-
Method for Detecting Core Malware Sites Related to Biomedical Information Systems
por: Kim, Dohoon, et al.
Publicado: (2015) -
Deep Feature Extraction and Classification of Android Malware Images
por: Singh, Jaiteg, et al.
Publicado: (2020) -
Windows malware detection based on static analysis with multiple features
por: Yousuf, Muhammad Irfan, et al.
Publicado: (2023) -
An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques
por: Albishry, Nabeel, et al.
Publicado: (2022) -
Automated Android Malware Detection Using User Feedback
por: Duque, João, et al.
Publicado: (2022)