DiRLU distills an A2C teacher into a lightweight student that detects BoT-IoT attacks at 99.6% accuracy with 2370 FLOPS and reversible post-hoc feature unlearning.
A novel method for malware detection using audio signal processing techniques,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Unlearning to Protect: A Distilled Reinforcement Learning Framework with Privacy-Preserving Feature Unlearning and XAI for IoT Security
DiRLU distills an A2C teacher into a lightweight student that detects BoT-IoT attacks at 99.6% accuracy with 2370 FLOPS and reversible post-hoc feature unlearning.