Ransomware Detection Using Multi-Vector Anomaly Profiling for Maximum Security

Abstract The every increasing sophistication of cyber threats requires innovative detection methodologies to safeguard digital infrastructures. Multi-Vector Anomaly Profiling (MVAP) emerges as a groundbreaking approach, offering a comprehensive analysis of system behaviors across multiple dimensions to identify subtle anomalies indicative of ransomware activity. MVAP integrates analyses of file system interactions, network traffic patterns, process execution sequences, and resource utilization metrics, constructing detailed anomaly profiles that enhance detection accuracy. Empirical evaluations demonstrate MVAP's efficacy in identifying diverse ransomware types, achieving high accuracy rates while maintaining low false positive and false negative occurrences. Comparative analyses underscore MVAP's superiority over traditional detection methods, highlighting its adaptability and robustness in addressing the evolving landscape of ransomware threats. The findings affirm MVAP's potential as a significant advancement in cybersecurity, providing a proactive and effective solution for mitigating the impact of ransomware attacks.