Advanced Autonomous Detection of Ransomware Using Dynamic Threat Pattern Recognition
Elena Mezheckaya, Marcus Robertson, Victor White, Edward Harrison · 2024
The escalating sophistication and frequency of cyber threats necessitate the development of advanced detection mechanisms capable of identifying and mitigating malicious activities with minimal human intervention. The Dynamic Threat Pattern Recognition (DTPR) system introduces an innovative approach to ransomware detection through the analysis of behavioral patterns, thereby addressing the limitations inherent in traditional signature-based methods. Employing a comprehensive dataset encompassing diverse ransomware variants and benign software, the DTPR system underwent rigorous evaluation to assess its detection accuracy, false positive rate, detection latency, resource utilization, and adaptability to emerging threats. The system achieved a detection accuracy of 97.8% and a false positive rate of 2.0%, demonstrating its precision in distinguishing malicious activities from legitimate processes. With an average detection latency of 0.45 seconds, the DTPR system facilitates prompt responses to ransomware incidents, thereby mitigating potential damage. The system's efficient resource utilization, characterized by moderate CPU and memory consumption under varying load conditions, demonstrates its suitability for deployment in environments with limited computational resources. Furthermore, the DTPR system exhibited adaptability to novel ransomware strains, successfully detecting 92.5% of previously unseen variants, highlighting its potential to address the evolving nature of cyber threats. Collectively, these findings affirm the DTPR system's capacity to significantly advance ransomware detection methodologies and enhance the overall security posture against sophisticated cyber threats.