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.

Ransomware is a type of malware that leverages encryption to execute its attacks. Its continuous evolution underscores its dynamic and ever-changing nature. The evolving variants use varying timelines to launch attacks and associate them with varying attack patterns. Detecting early evolving variants also leads to incomplete attack patterns. To develop an early detection model for behavioral drifting ransomware attacks, a detection model should be able to detect evolving ransomware variants. To consider the behavioral drifting problem of ransomware attacks, a model should be able to generalize the behavior of significant features comprehensively. Existing solutions were developed by using either a whole attack pattern or a fraction of an attack pattern. Likewise, they were also designed using historical data, which can make these solutions outdated or suffer from low accuracy for behavioral drift ransomware attacks. The detection models created using a fraction of the pre-encryption data also can not generalize the attack behavior of evolving ransomware variants. There is a need to develop an early detection model that can detect evolving ransomware variants with varying pre-encryption phases. The proposed model can detect the evolving ransomware variants by comprehensively generalizing significant attack patterns.