A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware

Mazen Gazzan, Bader Alobaywi, Mohammed Almutairi () and Frederick T. Sheldon ()
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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
Mohammed Almutairi: Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA
Frederick T. Sheldon: Department of Computer Science, College of Engineering, University of Idaho, Moscow, ID 83844, USA

Future Internet, 2025, vol. 17, issue 7, 1-55

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.

Keywords: ransomware; ransomware detection; early detection; cybersecurity; machine learning; generative adversarial networks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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