A Machine Learning-Based Ransomware Detection Method for Attackers’ Neutralization Techniques Using Format-Preserving Encryption
Ransomware, a type of malware that first appeared in 1989, encrypts user files and demands money for decryption, causing increasing global damage. To reduce the impact of ransomware, various file-based detection technologies are being developed; however, these have limitations, such as difficulties...
Saved in:
| Main Authors: | Jaehyuk Lee, Jinwook Kim, Hanjo Jeong, Kyungroul Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Analyzing TorrentLocker Ransomware Attacks: A Real Case Study
by: İlker Kara
Published: (2024-08-01) -
Ransomware detection and family classification using fine-tuned BERT and RoBERTa models
by: Amjad Hussain, et al.
Published: (2025-06-01) -
A comprehensive literature review on ransomware detection using deep learning
by: Er. Kritika
Published: (2025-12-01) -
Can Strategically Prioritizing Ransomware Protection and Implementing Specific Recommended Actions Potentially Reduce the Effects of a Ransomware Attack on an Organization?
by: Danielle Snyder
Published: (2023-09-01) -
A Wide and Weighted Deep Ensemble Model for Behavioral Drifting Ransomware Attacks
by: Umara Urooj, et al.
Published: (2025-03-01)