# Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

arXiv: https://arxiv.org/abs/1909.08092 · HTML: https://mlsanity.com/paper/1909.08092/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability](https://mlsanity.com/paper/2310.17944/) (2023)
- [StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2202.07568/) (2022)
- [RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT](https://mlsanity.com/paper/2212.14647/) (2022)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey](https://mlsanity.com/paper/2101.10121/) (2021)
- [ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes](https://mlsanity.com/paper/2505.23805/) (2025)
- [Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2302.00537/) (2023)
- [A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency](https://mlsanity.com/paper/2408.08934/) (2024)
- [Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstruction](https://mlsanity.com/paper/2503.01758/) (2025)
- [DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System](https://mlsanity.com/paper/2512.18616/) (2025)
- [Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense](https://mlsanity.com/paper/2610.06885/) (2026)

## Abstract (arXiv)

Reactive defense mechanisms, such as intrusion detection systems, have made significant efforts to secure a system or network for the last several decades. However, the nature of reactive security mechanisms has limitations because potential attackers cannot be prevented in advance. We are facing a reality with the proliferation of persistent, advanced, intelligent attacks while defenders are often way behind attackers in taking appropriate actions to thwart potential attackers. The concept of moving target defense (MTD) has emerged as a proactive defense mechanism aiming to prevent attacks. In this work, we conducted a comprehensive, in-depth survey to discuss the following aspects of MTD: key roles, design principles, classifications, common attacks, key methodologies, important algorithms, metrics, evaluation methods, and application domains. We discuss the pros and cons of all aspects of MTD surveyed in this work. Lastly, we highlight insights and lessons learned from this study and suggest future work directions. The aim of this paper is to provide the overall trends of MTD research in terms of critical aspects of defense systems for researchers who seek for developing proactive, adaptive MTD mechanisms.