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Titlebook: Decision and Game Theory for Security; 10th International C Tansu Alpcan,Yevgeniy Vorobeychik,György Dán Conference proceedings 2019 Spring

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Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals,sary can never achieve the targeted policy. We provide conditions on the falsified cost which can mislead the agent to learn an adversary’s favored policy. A numerical case study of water reservoir control is provided to show the potential hazards of RL in learning-based control systems and corroborate the results.
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Epistemic Terms and the Aims of Epistemologyto use in the forecast, while the adversary calculates a bias to inject in some sensors. We find an approximate equilibrium of the game and implement the defense mechanism using an ensemble of predictors, which introduces uncertainties that mitigate the attack’s impact. We evaluate our defense appro
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Vagueness and the Desiderata for Definitione different measures. Participants tended to invest preferably in the IDS, irrespective of the benefits from this investment. They were able to identify the firewall and insurance conditions in which investments were beneficial, but they did not invest optimally in these measures. The results imply
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https://doi.org/10.1007/978-1-4471-0471-1tructures to: (i) contribute towards creating attack data sets for training a SIEM (Security Information and Event Management) and (ii) to support post-incident forensics analysis by having recorded a collection of evidence regarding an attacker’s actions.
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https://doi.org/10.1007/978-1-4471-0471-1g scenarios. In such circumstances the secret sharing scheme facilitates a power sharing agreement in the society. We also state that non-reconstruction may be beneficial for this society and give several examples.
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