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Ransomware has become a pervasive threat in cybersecurity, causing significant financial and operational damage to organizations globally. The introduction of reverse Bayer optimization to the feature extraction and selection process represents a novel and significant advancement in ransomware detection, offering enhanced precision and recall by optimizing the distinctive characteristics of ransomware behavior. This research presents a comprehensive methodology that leverages genetic algorithms to fine-tune the parameters of feature extraction, resulting in a highly sensitive and reliable detection model capable of adapting to new and evolving ransomware strains. The experimental results demonstrate the effectiveness of the proposed system through rigorous evaluation metrics, including accuracy, precision, recall, and F1-score, highlighting its robustness in identifying true positive ransomware activities while minimizing false positives and negatives. Comparative analysis with existing techniques demonstrates the advantages of the proposed approach, particularly in its ability to provide a more adaptable and comprehensive detection mechanism. The findings contribute to the advancement of cybersecurity defenses, offering a scalable solution for real-world applications and setting the foundation for future innovations in malware detection methodologies. The study's limitations, such as dataset-specific reliance and potential overfitting, are acknowledged, with suggestions for future work to further enhance the generalizability and effectiveness of the detection system across diverse environments.
Wang et al. (Wed,) studied this question.