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.
Cyber Security and Applications 3, 100095
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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The paper maps LLM agent architectures onto a six-level continuum and argues that higher levels can enable simulation of emergent social phenomena while requiring attention to reproducibility and ethical issues.
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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Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research
The paper maps LLM agent architectures onto a six-level continuum and argues that higher levels can enable simulation of emergent social phenomena while requiring attention to reproducibility and ethical issues.
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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.