Algorithmic Segmentation and Behavioral Profiling for Ransomware Detection Using Temporal-Correlation Graphs
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
The rapid evolution of cyber threats has outpaced traditional detection
methodologies, necessitating innovative approaches capable of addressing the
adaptive and complex behaviors of modern adversaries. A novel framework was
introduced, leveraging Temporal-Correlation Graphs to model the intricate
relationships and temporal patterns inherent in malicious operations. The
approach dynamically captured behavioral anomalies, offering a robust mechanism
for distinguishing between benign and malicious activities in real-time
scenarios. Extensive experiments demonstrated the framework's effectiveness
across diverse ransomware families, with consistently high precision, recall,
and overall detection accuracy. Comparative evaluations highlighted its better
performance over traditional signature-based and heuristic methods,
particularly in handling polymorphic and previously unseen ransomware variants.
The architecture was designed with scalability and modularity in mind, ensuring
compatibility with enterprise-scale environments while maintaining resource
efficiency. Analysis of encryption speeds, anomaly patterns, and temporal
correlations provided deeper insights into the operational strategies of
ransomware, validating the framework's adaptability to evolving threats. The
research contributes to advancing cybersecurity technologies by integrating
dynamic graph analytics and machine learning for future innovations in threat
detection. Results from this study underline the potential for transforming the
way organizations detect and mitigate complex cyberattacks.
