The Hierarchical Entropy-Based Analysis (HEBA) framework introduces an innovative approach to ransomware detection through the application of advanced entropy metrics and hierarchical data examination. By employing multifaceted entropy measures, including Shannon entropy, conditional entropy, and hybrid metrics, HEBA effectively identifies anomalous patterns indicative of malicious encryption activities. The framework's probabilistic assessment of deviations from established benign profiles enhances detection precision, thereby minimizing false positives. Comprehensive evaluations demonstrate HEBA's high detection accuracy and low false positive rates across diverse ransomware variants. Resource utilization assessments indicate minimal impact on system performance, affirming the framework's operational efficiency. Detection latency measurements reveal prompt identification of ransomware activities, enabling timely intervention. The analysis of entropic patterns provides deeper insights into ransomware encryption behaviors, enhancing threat intelligence. Comparative analyses highlight HEBA's superiority over traditional detection methods, showing its potential to advance cybersecurity defenses. The findings contribute to the broader understanding of entropy-based detection mechanisms and their practical applications in combating sophisticated cyber threats.