Crypto-ransomware detection using machine learning models in file-sharing network scenario with encrypted traffic
This addresses ransomware threats for enterprises using file-sharing networks, offering a detection tool that works with encrypted traffic, though it is incremental as it applies existing ML models to a specific scenario.
The paper tackled ransomware detection in file-sharing networks by analyzing traffic patterns with machine learning, achieving detection of all 70 ransomware binaries from 26 strains, including unseen ones, with validation on false positive rates and encryption limits.
Ransomware is considered as a significant threat for most enterprises since the past few years. In scenarios wherein users can access all files on a shared server, one infected host can lock the access to all shared files. We propose a tool to detect ransomware infection based on file-sharing traffic analysis. The tool monitors the traffic exchanged between the clients and the file servers and using machine learning techniques it searches for patterns in the traffic that betray ransomware actions while reading and overwriting files. The proposal is designed to work for clear text and for encrypted file-sharing protocols. We compare three machine learning models and choose the best for validation. We train and test the detection model using more than 70 ransomware binaries from 26 different strains and more than 2500 hours of not infected traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries, including those not used in training phase (unseen). This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detected.