A Wide and Weighted Deep Ensemble Model for Behavioral Drifting Ransomware Attacks

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 Likewise, they were also designed using historical data, which can make these solutions outdated or suffer from low accuracy for behavioral drift ransomware attacks.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.