Innovative Framework for Ransomware Detection Using Adaptive Cryptographic Behavior Analysis
Gene Shanks, Montgomery Sterling, Nicholas Harrington, Oliver Fitzwilliam, Quentin Radcliffe · 2024
The increasing sophistication of cryptographic techniques employed by malicious actors has necessitated the development of advanced detection frameworks capable of identifying ransomware with high precision. Adaptive Cryptographic Behavior Analysis (ACBA) offers a novel approach by profiling system-level cryptographic activities to detect anomalous behaviors associated with ransomware. Through mathematical modeling and machine learning-driven anomaly detection, the framework leverages adaptive profiling mechanisms to enhance its responsiveness to previously unseen threats. Rigorous experimentation demonstrated its capacity to achieve a detection accuracy of 98.7%, coupled with a low false positive rate of 1.2%, showing its reliability across diverse operational environments. Comparative analyses revealed the superiority of ACBA over traditional detection methods, particularly in terms of scalability and adaptability. Real-time deployment scenarios validated its computational efficiency, ensuring minimal resource consumption while maintaining robust detection capabilities. Insights gained from resource utilization and scalability assessments further solidify its potential for widespread implementation. The integration of cryptographic behavior analysis within a structured algorithmic framework establishes a significant advancement in proactive cybersecurity measures against ransomware.