# A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

arXiv: https://arxiv.org/abs/1712.05441 · HTML: https://mlsanity.com/paper/1712.05441/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2017.

## Built on by (most cited first, 22 of 22)

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- [Domain-Independent Deception: A New Taxonomy and Linguistic Analysis](https://mlsanity.com/paper/2402.01019/) (2024)
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- [Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey](https://mlsanity.com/paper/2101.10121/) (2021)
- [Multi-defender Security Games with Schedules](https://mlsanity.com/paper/2311.16392/) (2023)
- [Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense](https://mlsanity.com/paper/2512.23849/) (2025)
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- [System Component-Level Self-Adaptations for Security via Bayesian Games](https://mlsanity.com/paper/2103.08673/) (2021)
- [Cyber Deception: State of the art, Trends and Open challenges](https://mlsanity.com/paper/2409.07194/) (2024)
- [CyGATE: Game-Theoretic Cyber Attack-Defense Engine for Patch Strategy Optimization](https://mlsanity.com/paper/2508.00478/) (2025)
- [On the Trade-Off Between Transparency and Security in Adversarial Machine Learning](https://mlsanity.com/paper/2511.11842/) (2025)
- [DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System](https://mlsanity.com/paper/2512.18616/) (2025)
- [Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows](https://mlsanity.com/paper/2606.14769/) (2026)
- [A Variational Framework for LLM Generator-Regulator Games](https://mlsanity.com/paper/2606.18424/) (2026)
- [Reading Is Not Leaking: Local, Auditable Measurement and Reduction of Inference Exposure from Public Footprints](https://mlsanity.com/paper/2609.32565/) (2026)

## Abstract (arXiv)

Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.