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E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based...

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_1b7a1a6d62d841dd96d1158dd50f2221

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches

About this item

Full title

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches

Publisher

Switzerland: MDPI AG

Journal title

Sensors (Basel, Switzerland), 2023-05, Vol.23 (9), p.4467

Language

English

Formats

Publication information

Publisher

Switzerland: MDPI AG

More information

Scope and Contents

Contents

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an effi...

Alternative Titles

Full title

E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_1b7a1a6d62d841dd96d1158dd50f2221

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_1b7a1a6d62d841dd96d1158dd50f2221

Other Identifiers

ISSN

1424-8220

E-ISSN

1424-8220

DOI

10.3390/s23094467

How to access this item