A New Approach to Ransomware Detection Using Hierarchical Entropic Pattern Analysis

The escalating sophistication of cyber threats, characterized by increasingly complex attack vectors and adaptive malicious software, requires the development and implementation of innovative detection methodologies that can effectively identify and mitigate these evolving threats. The Hierarchical Entropic Pattern Analysis (HEPA) framework offers a novel approach to identifying ransomware activities through multi-layered entropy analysis. By examining entropy variations across hierarchical data structures, HEPA effectively distinguishes between benign and malicious behaviors. The system's modular design ensures seamless integration with existing cybersecurity infrastructures, promoting scalability and adaptability. Comprehensive evaluations demonstrate HEPA'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 HEPA'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.