Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

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A Deception Planning Framework for Cyber Defense

The role and significance of deception systems such as honeypots for slowing down attacks and collecting their signatures are well-known. However, the focus has primarily been on developing individual deception systems, and very few works have focused on developing strategies for a synergistic and strategic combination of these systems to achieve more ambitious deception goals. The objective of this paper is to lay a scientific foundation for cyber deception planning, by (1) presenting a formal deception logic for modeling cyber deception, and (2) introducing a deception framework that augments this formal modeling with necessary quantitative reasoning tools to generate coordinated deception plans. To show expressiveness and evaluate effectiveness and overhead of the framework, we use it to model and solve two important deception planning problems: (1) strategic honeypot planning, and (2) deception planning against route identification. Through these case studies, we show that the generated deception plans are highly effective and outperform alternative random and unplanned deception strategies