Triple-Layer Bayesian Euclidean Curve Algorithm for Automated Ransomware Classification

Ransomware attacks have emerged as a significant threat in cybersecurity, exploiting vulnerabilities across various systems and infrastructures. The need for highly accurate, automated classification techniques has become paramount to combat the rapid evolution of ransomware. The Triple-Layer Bayesian Euclidean Curve Algorithm offers a novel approach by combining probabilistic inference with distance-based methods, enabling more precise identification of ransomware families even when dealing with complex or overlapping feature spaces. Through its multi-layered structure, the algorithm refines classification decisions incrementally, significantly reducing misclassification rates, particularly for ransomware samples that employ obfuscation or other evasion techniques. Evaluation results demonstrate the algorithm’s superior performance in terms of precision, recall, and accuracy compared to traditional machine learning models, while also exhibiting strong generalization capabilities for unseen ransomware variants. This methodology addresses key challenges in ransomware classification by improving both detection accuracy and the algorithm's adaptability to emerging threats, presenting a robust solution to the growing problem of ransomware in cybersecurity.