Enhanced Vectorized Ransomware Detection: A Novel Spectral Segmentation Approach Using Nonlinear Frequency Patterns
Elvis Bennett, Jasper Ellington, Gideon Blackstone, Hugo Whitfield, Leon Ashcroft · 2024
The escalating sophistication of cyber threats necessitates the development of advanced detection mechanisms to safeguard digital infrastructures. The Spectral Segmentation Detection (SSD) model introduces an innovative approach to ransomware detection through the analysis of frequency patterns inherent in malicious activities. By employing spectral segmentation algorithms and nonlinear feature vectorization, the SSD model effectively identifies and isolates unique ransomware behaviors, thereby enhancing detection accuracy and reducing false positives. Empirical evaluations demonstrate the model's robustness across various ransomware variants, highlighting its adaptability and potential for broader application in cybersecurity defenses. The integration of frequency pattern analysis within the SSD framework offers a comprehensive methodology for identifying ransomware, setting a new standard for future developments in this critical area.