The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.
CSLE: A Reinforcement Learning Platform for Au- tonomous Security Management
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CSLE combines emulation and simulation to train and validate reinforcement learning agents for autonomous security management in conditions closer to real systems than pure simulation.
citing papers explorer
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Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
The paper releases two adversarial malware datasets (44k family-labelled, 33k type-labelled) with high evasion rates and demonstrates that 0.5% poisoning injection raises evasion from 26.1% to 92.8%.
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CSLE: A Reinforcement Learning Platform for Autonomous Security Management
CSLE combines emulation and simulation to train and validate reinforcement learning agents for autonomous security management in conditions closer to real systems than pure simulation.