Adversarial RL approximates a game-theoretic equilibrium to yield a stochastic policy for prioritizing alerts against adaptive attackers in fraud and intrusion detection.
Understanding the difficulty of training deep feedforward neural networks,
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Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
Adversarial RL approximates a game-theoretic equilibrium to yield a stochastic policy for prioritizing alerts against adaptive attackers in fraud and intrusion detection.