NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
Computers & Security 140, 103786
2 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
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2026 2representative citing papers
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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Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
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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.