Neural Cryptographic Fingerprinting for Autonomous Ransomware Detection

This study introduces an innovative approach to ransomware detection, utilizing cryptographic fingerprinting to identify malicious encryption behaviors with unprecedented precision. The proposed framework leverages deep neural networks to analyze high-dimensional patterns extracted from cryptographic operations, enabling the differentiation between benign and ransomware-related activities. Experimental evaluations demonstrate its capability to achieve a detection accuracy exceeding 97\%, even under challenging conditions involving noisy and incomplete data. Comparative analysis highlights its superiority over traditional signature-based and heuristic methods, particularly in addressing advanced evasion tactics such as polymorphism and code obfuscation. The integration of convolutional and recurrent layers, coupled with attention mechanisms, ensures the system’s adaptability to diverse ransomware families and operational environments. Resource utilization metrics confirm its scalability and efficiency, making it suitable for deployment in enterprise and critical infrastructure networks. Latency analysis indicates rapid detection times, with an average response of 2.1 seconds, minimizing potential damage during ransomware incidents. The robustness of the framework under simulated real-world conditions validates its practicality for large-scale applications. By focusing on the cryptographic essence of ransomware, the framework addresses limitations in traditional detection systems and provides a scalable, autonomous solution to evolving cyber threats.