SHIELD: A Host-Independent Framework for Ransomware Detection using Deep Filesystem Features
Md Raz, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy, Farshad Khorrami, Ramesh Karri

TL;DR
SHIELD is a host-independent framework that uses filesystem-level features and machine learning to detect ransomware activity in real-time, providing a tamper-proof, deployable solution that effectively identifies and mitigates ransomware threats.
Contribution
This work introduces a novel filesystem-layer based detection framework that is host-independent, tamper-proof, and capable of real-time ransomware detection and mitigation, including deployment on storage controllers.
Findings
Achieves 97.29% accuracy in binary ransomware detection.
Hardware-only features retain 95.97% accuracy, enabling FPGA/ASIC deployment.
Halts disk operations within tens of actions, limiting affected files to <0.4%.
Abstract
Ransomware's escalating sophistication necessitates tamper-resistant, off-host detection solutions that capture deep disk activity beyond the reach of a compromised operating system. Existing detection systems use host/kernel signals or rely on coarse block-I/O statistics, which are easy to evade and miss filesystem semantics. The filesystem layer itself remains underexplored as a source of robust indicators for storage-controller-level defense. To address this, we present SHIELD: a Secure Host-Independent Extensible Metric Logging Framework for Tamper-Proof Detection and Real-Time Mitigation of Ransomware Threats. SHIELD parses and logs filesystem-level features that cannot be evaded or obfuscated to expose deep disk activity for real-time ML-based detection and mitigation. We evaluate the efficacy of these metrics through experiments with both binary (benign vs. malicious behavior)…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Digital and Cyber Forensics
