Constructing Resilient Frameworks for Autonomous Ransomware Detection Using Algorithmic Behavior Profiling

Ransomware has become a significant cybersecurity threat, leveraging advanced encryption techniques to disrupt critical systems and extort payments. Existing detection methods, such as signature-based, anomaly-based, and heuristic approaches, face limitations in addressing the increasing sophistication and obfuscation of ransomware. This study presents a novel ransomware detection framework based on algorithmic behavior profiling. Unlike traditional methods, the framework autonomously identifies ransomware-specific patterns through advanced behavioral analysis, feature engineering, and machine learning, without reliance on predefined signatures or extensive labeled datasets. The system integrates modular architecture, dynamic profiling, and ensemble decision-making to enhance detection accuracy while minimizing false positives and computational overhead. Experimental results demonstrate the framework’s effectiveness across diverse ransomware families, achieving high detection rates with low latency, even under constrained resource environments. Furthermore, the system exhibits robustness in identifying novel ransomware variants and scalability across distributed deployment scenarios. This research offers a significant advancement in ransomware detection, providing a reliable, efficient, and adaptive solution to modern cybersecurity challenges.