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Flipit Game Deception Strategy Selection Method Based on Deep Reinforcement Learning

The authors present a method for selecting cyber deception strategies using a multi-stage Flipit game, aiming to account for defense timing alongside spatial strategy selection. The approach constructs a spatiotemporal decision-making model, introduces a discount factor and transition probabilities, and applies a Proximal Policy Optimization algorithm to compute defender strategies.

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What we know

The authors present a method for selecting cyber deception strategies using a multi-stage Flipit game, aiming to account for defense timing alongside spatial strategy selection. The approach constructs a spatiotemporal decision-making model, introduces a discount factor and transition probabilities, and applies a Proximal Policy Optimization algorithm to compute defender strategies.

Evidence limits

This review is based only on the Crossref deposited abstract; the full text was not examined and results were not independently validated. Effectiveness and advantages mentioned are as attributed by the authors.

Reviewed: author abstract. Full-text results were not analyzed.

Sources and provenance

  1. Flipit Game Deception Strategy Selection Method Based on Deep Reinforcement Learning
    abstract · 2026-10-11

Reviewed: 2026-10-13. This record may change when new evidence is found.

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