CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments
Shenao Wang, Feng Dong, Hangfeng Yang, Jingheng Xu, Haoyu Wang

TL;DR
CanCal is a real-time, lightweight ransomware detection system designed for industrial environments, significantly reducing system overhead and alert fatigue while maintaining high detection accuracy and rapid response.
Contribution
It introduces a selective filtering and behavioral analysis approach that minimizes overhead and alert fatigue, enabling effective real-time ransomware detection in large-scale industrial settings.
Findings
Achieves detection within 30ms and response within 3 seconds.
Reduces CPU utilization by over 90%.
Successfully detected and thwarted 61 ransomware attacks in real-world deployment.
Abstract
Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous proposed detection and defense methods, existing approaches face two fundamental limitations in large-scale industrial applications: intolerable system overheads and notorious alert fatigue. To address these challenges, we propose CanCal, a real-time and lightweight ransomware detection system. Specifically, CanCal selectively filters suspicious processes by the monitoring layers and then performs in-depth behavioral analysis to isolate ransomware activities from benign operations, minimizing alert fatigue while ensuring lightweight computational and storage overhead. The experimental results on a large-scale industrial environment~(1,761 ransomware, ~3 million events, continuous test over 5 months) indicate that CanCal is as effective as state-of-the-art techniques while enabling…
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