arXiv AI
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning
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A Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage
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Cost-Aware Hierarchical Multi-Agent Ransomware Detection and Family Attribution
The paper introduces a Cost-Aware Hierarchical Multi-Agent System (HMAS) for ransomware detection and family attribution that adaptively selects analysis modalities to balance accuracy and computational cost. Static analysis is used first, with dynamic and memory modalities added only when confidence is low or specialist agents disagree, guided by a cost model. Experiments show HMAS achieves high accuracy (96.57% binary detection, 0.90 macro‑F1 attribution) while reducing analysis cost by 43.97% and latency, with 56.05% of cases resolved using static evidence alone.