Author
Listed:
- Mazen Gazzan
(Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia)
- Bader Alobaywi
(Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39923, Saudi Arabia)
- Mohammed Almutairi
(Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
College of Computer Science and Engineering, University of Hafr Al Batin, Hafar Al Batin 39923, Saudi Arabia)
- Frederick T. Sheldon
(Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA)
Abstract
Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy and minimize false positives. The framework addresses the limitations of existing methods by integrating operational data with situational and threat intelligence, enabling it to dynamically adapt to the evolving ransomware landscape. Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. The model demonstrates a 4% improvement in detection accuracy (from 90% to 94%) through synthetic data generation and reduces false positives from 15.4% to 14%. The IMIS technique further increases accuracy to 96% while reducing false positives. The UA-DES optimization boosts accuracy to 98.6% and lowers false positives to 10%. Overall, this framework effectively addresses the challenges posed by evolving ransomware, significantly enhancing detection accuracy and reliability.
Suggested Citation
Mazen Gazzan & Bader Alobaywi & Mohammed Almutairi & Frederick T. Sheldon, 2025.
"A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware,"
Future Internet, MDPI, vol. 17(7), pages 1-55, July.
Handle:
RePEc:gam:jftint:v:17:y:2025:i:7:p:311-:d:1704225
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:311-:d:1704225. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.