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This work investigates an evolutionary approach to generate gaming strategies for the Attacker-Defender or Intruder-Administrator in simulated cyber warfare. Given a network environment, attack graphs are defined in an anticipation game framework to generate action strategies by analyzing (local/global) vulnerabilities and security measures. The proposed approach extends an Anticipation Game (AG) framework by taking into account multiple conflicting objectives like cost, time, reward and performance for generating effective gaming strategies. A gaming strategy represents a sequence of decision rules that an attacker or the defender can employ to achieve his/her desired goal.
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