Decentralized Entropy-Driven Ransomware Detection Using Autonomous Neural Graph Embeddings
Journal:
arXiv
Published Date:
Feb 11, 2025
Abstract
The increasing sophistication of cyber threats has necessitated the
development of advanced detection mechanisms capable of identifying and
mitigating ransomware attacks with high precision and efficiency. A novel
framework, termed Decentralized Entropy-Driven Detection (DED), is introduced,
leveraging autonomous neural graph embeddings and entropy-based anomaly scoring
to address the limitations of traditional methods. The framework operates on a
distributed network of nodes, eliminating single points of failure and
enhancing resilience against targeted attacks. Experimental results demonstrate
its ability to achieve detection accuracy exceeding 95\%, with false positive
rates maintained below 2\% across diverse ransomware variants. The integration
of graph-based modeling and machine learning techniques enables the framework
to capture complex system interactions, facilitating the identification of
subtle anomalies indicative of ransomware activity. Comparative analysis
against existing methods highlights its superior performance in terms of
detection rates and computational efficiency. Case studies further validate its
effectiveness in real-world scenarios, showcasing its ability to detect and
mitigate ransomware attacks within minutes of their initiation. The proposed
framework represents a significant step forward in cybersecurity, offering a
scalable and adaptive solution to the growing challenge of ransomware
detection.
