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.
Computers & Security 148, 104180
2 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
2
Pith papers citing it
7
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
Directed droplet motion via surface property gradients provides a versatile approach for fluid transport in applications like digital microfluidics and bio-diagnostics.
citing papers explorer
-
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.
-
Directed droplet motion -- Its versatile nature and anticipated applications
Directed droplet motion via surface property gradients provides a versatile approach for fluid transport in applications like digital microfluidics and bio-diagnostics.