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System-Wide Ransomware Attack Detection via Perturbation-Resilient Representation Learning
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Summary
CoLPR is a contrastive self-supervised framework for system-wide ransomware detection that learns perturbation-resilient representations, so it can separate benign-malicious traits from interference caused by concurrently running applications. Its Inborn augmentation strategy executes realistic application combinations and builds positive pairs with the same co-participants under diverse perturbations. The authors report 100% recall, 98.7% accuracy and an average detection delay of 5.31 seconds across three deployment environments, outperforming baselines by 5%–10% under multi-application interference.