By analyzing network traffic patterns between clients and file servers, you can detect ransomware attacking shared storage earlier and more reliably than traditional endpoint-only detection, even when the malware doesn't show obvious signs on the server itself.
This paper presents a hybrid detection system for crypto-ransomware targeting shared storage in enterprise networks. It combines signature-based detection (using network traffic indicators) with machine learning to catch ransomware before it encrypts files, achieving 99.64% precision with zero false negatives.