Integrating DDQN-based ransomware detection with multi-shard SISA enables privacy-compliant sample removal in 5-30 seconds while preserving F1 > 0.99 and limiting membership inference leakage.
Improving ransomware detection based on portable executable header using xception convolutional neural network
4 Pith papers cite this work, alongside 55 external citations. Polarity classification is still indexing.
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Embedding a 4:1 false-negative-to-false-positive penalty ratio into DRL reward signals reduces missed ransomware detections by 43% relative to symmetric rewards across 480 controlled runs.
RansomTrack hybrid framework detects ransomware at 96% accuracy in under 10 seconds via Radare2 static features, Frida dynamic behaviors, and ensemble ML on a public 165-family dataset.
TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.
citing papers explorer
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Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning
Integrating DDQN-based ransomware detection with multi-shard SISA enables privacy-compliant sample removal in 5-30 seconds while preserving F1 > 0.99 and limiting membership inference leakage.
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SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design
Embedding a 4:1 false-negative-to-false-positive penalty ratio into DRL reward signals reduces missed ransomware detections by 43% relative to symmetric rewards across 480 controlled runs.
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RansomTrack: A Hybrid Behavioral Analysis Framework for Ransomware Detection
RansomTrack hybrid framework detects ransomware at 96% accuracy in under 10 seconds via Radare2 static features, Frida dynamic behaviors, and ensemble ML on a public 165-family dataset.
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TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats
TL-RL-FusionNet uses frozen transfer learning backbones and a Q-learning agent to adaptively reweight training samples for ransomware detection, reporting 99.1% accuracy on a 1000-sample dataset.