Decentralized Entropy-Based Ransomware Detection Using Autonomous Feature Resonance

TL;DR

This paper introduces Autonomous Feature Resonance, a decentralized entropy-based method for ransomware detection that achieves high accuracy, low false rates, and adaptability across diverse ransomware families, improving cybersecurity defenses.

Contribution

It presents a novel decentralized entropy-based detection approach with self-learning capabilities, outperforming traditional methods in accuracy and scalability.

Findings

Detection accuracy of 97.3%

False positive rate of 1.8%

Effective across multiple ransomware families

Abstract

The increasing sophistication of cyber threats has necessitated the development of advanced detection mechanisms capable of identifying malicious activities with high precision and efficiency. A novel approach, termed Autonomous Feature Resonance, is introduced to address the limitations of traditional ransomware detection methods through the analysis of entropy-based feature interactions within system processes. The proposed method achieves an overall detection accuracy of 97.3\%, with false positive and false negative rates of 1.8\% and 2.1\%, respectively, outperforming existing techniques such as signature-based detection and behavioral analysis. Its decentralized architecture enables local processing of data, reducing latency and improving scalability, while a self-learning mechanism ensures continuous adaptation to emerging threats. Experimental results demonstrate consistent performance across diverse ransomware families, including LockBit 3.0, BlackCat, and Royal, with low detection latency and efficient resource utilization. The method's reliance on entropy as a distinguishing feature provides robustness against obfuscation techniques, making it suitable for real-time deployment in high-throughput environments. These findings highlight the potential of entropy-based approaches to enhance cybersecurity frameworks, offering a scalable and adaptive solution for modern ransomware detection challenges.