Adaptive Behavior-Based Ransomware Detection via Dynamic Flow Signatures

Ransomware continues to pose a significant threat to both individuals and organizations, evolving with sophisticated tactics that bypass traditional detection methods. The introduction of Adaptive Behavior-Based Ransomware Detection (ABRD) presents a dynamic solution capable of identifying ransomware through real-time behavioral analysis, bypassing the limitations of signature-based techniques. ABRD operates through the extraction of flow signatures, capturing operational characteristics of ransomware attacks, which allows for the detection of novel and zero-day variants without relying on predefined signatures. The system utilizes machine learning models to analyze these behavioral patterns, continuously adapting to emerging threats and ensuring high detection accuracy. Experimental evaluations demonstrated ABRD's effectiveness in handling encrypted communications, minimizing false positives, and scaling efficiently across network environments. Its ability to detect ransomware in real-time, combined with adaptive learning capabilities, positions ABRD as a powerful tool for automating cybersecurity defenses and addressing the challenges posed by the constantly evolving landscape of ransomware attacks.