A game-theoretic framework quantifies when cyber deception pays: deception raises defender utility versus matched non-deceptive baselines but its value erodes as system observability increases, with closed-form break-even conditions and parameter regimes where simple heuristics nearly match optimum.

Main Finding

The paper provides a principled, game-theoretic framework to measure and compare the operational value of cyber deception relative to a matched non-deceptive baseline, and to quantify how that value degrades as the true system state becomes more observable. It introduces two operational metrics—value of deception and price of transparency—derives defender-optimal strategies and bounds (including break-even conditions), and links equilibrium outcomes to an information-theoretic measure of residual attacker uncertainty. Computational experiments show robust tradeoffs among decoy realism, budget, and attacker rationality, and identify parameter regimes where simple allocation heuristics approximate optimal policies.

Key Points

• Paired-game design: each defender–attacker interaction is represented by a baseline (no deception) game and a matched decoy-enabled deception game, enabling direct, causal measurement of deception impact.
• Two operational metrics:
  • Value of deception: the change in defender equilibrium utility attributable to deception, measured relative to the baseline game.
  • Price of transparency: the marginal loss in deception value induced by increased observability of the true system state.
• Analytical contributions:
  • Characterization of defender-optimal deception allocations.
  • Closed-form bounds and break-even conditions delineating when deception is ineffective due to cost or detectability.
  • Approximation/approximation-accuracy results that justify scalable allocation rules and heuristics.
• Information-theoretic mechanism: an uncertainty construct (entropy-like) captures how much attacker uncertainty remains after observation, explaining mechanistically why deception value falls as transparency increases.
• Computational results:
  • Experiments across heterogeneous scenarios produce consistent cross-setting comparability of the proposed metrics.
  • Tradeoffs highlighted between decoy realism (how convincing decoys are), defender budget constraints, and attacker rationality/modeling assumptions.
  • Identification of regimes where simple heuristics nearly match optimal allocations, supporting practical deployment.

Data & Methods

• Theoretical model:
  • Paired strategic games (baseline vs deception) modeling defender and attacker payoffs and informational structure.
  • Equilibrium solution concepts used to compute defender and attacker utilities under each game (equilibrium type stated in the paper).
  • Definition and formalization of value of deception and price of transparency as baseline-referenced equilibrium utility differences and derivatives with respect to observability.
• Analytical work:
  • Derivation of defender-optimal strategies under resource constraints.
  • Proofs of bounds and break-even conditions tying deception costs, detectability, and observability to value.
  • Approximation guarantees showing when simple allocation rules achieve provable performance relative to optimal.
• Information-theoretic component:
  • Construction of an uncertainty metric (entropy/mutual-information style) measuring residual attacker uncertainty after observations; used to connect mechanism-level effects to equilibrium utility changes.
• Computational experiments:
  • Simulated heterogeneous scenarios sweeping parameters: decoy realism, budget levels, attacker rationality (degree of optimality/learning), and observability/transparency levels.
  • Evaluation metrics: value of deception, price of transparency, defender utility, and heuristic vs optimal performance gaps.
  • Sensitivity analyses to identify robust regimes and tradeoffs.

Implications for AI Economics

• Valuing defensive AI investments: the value-of-deception metric provides a direct, game-theoretic way to monetize the benefit of deception technologies relative to non-deceptive alternatives, supporting investment decisions and cost–benefit comparisons.
• Pricing and incentives around transparency: the price-of-transparency formalizes how increased observability (from regulation, disclosure, or transparency tools) can reduce the effectiveness of deception-based defenses, informing policy tradeoffs between transparency and security.
• Resource allocation and mechanism design: the approximation results and identified heuristic regimes enable scalable, economically efficient allocation of limited defensive resources in large-scale systems where computing exact optima is infeasible.
• Heterogeneity and attacker modeling: results emphasize that defender returns depend critically on attacker rationality and information-processing; economic models of cybersecurity should incorporate strategic heterogeneity and bounded rationality for accurate valuation.
• Externalities and market outcomes: because deception effectiveness declines with transparency and attacker learning, there can be strategic externalities across firms and platforms (e.g., if one actor’s disclosures reduce the market value of deception-based defenses elsewhere), suggesting roles for coordination or insurance markets.
• Link to information economics: the information-theoretic uncertainty measure connects to classical value-of-information concepts, offering a bridge between mechanism-level security analysis and economic theories of information, signaling, and screening.