# Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey

arXiv: https://arxiv.org/abs/2101.10121 · HTML: https://mlsanity.com/paper/2101.10121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 180 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 19 of 19)

- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Privacy-preserving Machine Learning through Data Obfuscation](https://mlsanity.com/paper/1807.01860/) (2018)
- [Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense](https://mlsanity.com/paper/1909.08092/) (2019)
- [A Survey of Game Theoretic Approaches for Adversarial Machine Learning in Cybersecurity Tasks](https://mlsanity.com/paper/1912.02258/) (2019)
- [The Internet of Battle Things](https://mlsanity.com/paper/1712.08980/) (2017)
- [Privacy Games: Optimal User-Centric Data Obfuscation](https://mlsanity.com/paper/1402.3426/) (2014)
- [Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception](https://mlsanity.com/paper/1809.02013/) (2018)
- [Deceptive Labeling: Hypergames on Graphs for Stealthy Deception](https://mlsanity.com/paper/2004.05213/) (2020)
- [Political Bots and the Manipulation of Public Opinion in Venezuela](https://mlsanity.com/paper/1507.07109/) (2015)
- [Modeling and Analysis of Leaky Deception using Signaling Games with Evidence](https://mlsanity.com/paper/1804.06831/) (2018)
- [Decoy Allocation Games on Graphs with Temporal Logic Objectives](https://mlsanity.com/paper/2010.01208/) (2020)
- [Deception by Design: Evidence-Based Signaling Games for Network Defense](https://mlsanity.com/paper/1503.05458/) (2015)
- [Flip the Cloud: Cyber-Physical Signaling Games in the Presence of Advanced Persistent Threats](https://mlsanity.com/paper/1507.00576/) (2015)
- [Cyber Deception for Computer and Network Security: Survey and Challenges](https://mlsanity.com/paper/2007.14497/) (2020)
- [SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach](https://mlsanity.com/paper/1701.01676/) (2017)
- [Deception-As-Defense Framework for Cyber-Physical Systems](https://mlsanity.com/paper/1902.01364/) (2019)
- [Learning and Planning in the Feature Deception Problem](https://mlsanity.com/paper/1905.04833/) (2019)
- [Optimizing Vulnerability-Driven Honey Traffic Using Game Theory](https://mlsanity.com/paper/2002.09069/) (2020)
- [Online Social Deception and Its Countermeasures for Trustworthy Cyberspace: A Survey](https://mlsanity.com/paper/2004.07678/) (2020)

## Built on by (most cited first, 1 of 1)

- [AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices](https://mlsanity.com/paper/2303.12367/) (2023)

## Abstract (arXiv)

Defensive deception is a promising approach for cyber defense. Via defensive deception, the defender can anticipate attacker actions; it can mislead or lure attacker, or hide real resources. Although defensive deception is increasingly popular in the research community, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.