A Novel Algorithmic Framework for Autonomous Ransomware Detection Using Multi-Layer Temporal Anomaly Patterns
Julja Denis, James Featherstone, Olivia Beaufort, Henry Edelstein · 2024
The increasing complexity of cyberattacks demands innovative detection methodologies capable of addressing evolving threats with precision and adaptability. Multi-Layer Temporal Anomaly Patterns (ML-TAP) offer a groundbreaking framework for detecting ransomware through layered temporal analysis, isolating subtle deviations in behavioral patterns indicative of malicious activity. The integration of hierarchical anomaly detection enables the identification of both short-term and long-term temporal irregularities, ensuring robust detection even in the presence of polymorphic or obfuscated ransomware variants. Unlike traditional methodologies reliant on static signatures or manually engineered features, the proposed framework autonomously refines its detection criteria through iterative analysis of high-dimensional data streams. The empirical evaluation demonstrates high detection accuracy, with consistently low false-positive rates across diverse ransomware strains, including LockBit, Hive, and Conti, under varied experimental conditions. Computational efficiency, a critical aspect for real-time deployment, is achieved through a modular and scalable system architecture designed to accommodate varying data volumes and resource constraints. Comparative assessments reveal that the ML-TAP framework outperforms baseline methods, showcasing its resilience against both known and emerging ransomware techniques. The incorporation of advanced clustering and temporal modeling strategies ensures that anomalies are captured and analyzed comprehensively across multiple layers of abstraction. Detailed performance metrics validate the framework’s adaptability, with results indicating robust operation across diverse environmental scenarios, from isolated systems to enterprise-wide networks. The research establishes a new paradigm in autonomous cybersecurity solutions, addressing the limitations of existing approaches through the innovative application of temporal analytics. Practical implications include enhanced early detection capabilities and mitigation of ransomware impacts through real-time response mechanisms, positioning the framework as a transformative solution in proactive cybersecurity practices.