Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense
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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.
Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). First posted on arXiv 12 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 2; 2025: 3; 2026: 1.
12 papers.
- 2022A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networkscited by 11
- 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
- 2022StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detectioncited by 3
- 2022RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoTcited by 2
- 2024The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Surveycited by 2
- 2021Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Surveycited by 1
- 2025ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetescited by 1
- 2023Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detectioncited by 0
- 2024A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiencycited by 0
- 2025Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstructioncited by 0
- 2025DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming Systemcited by 0
- 2026Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defensecited by 0
- 2026Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense20 Sep 2026
- 2025DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System21 Dec 2025
- 2025ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes27 May 2025
- 2025Zero-Trust Artificial Intelligence Model Security Based on Moving Target Defense and Content Disarm and Reconstruction3 Mar 2025
- 2024A Factored MDP Approach To Moving Target Defense With Dynamic Threat Modeling and Cost Efficiency16 Aug 2024
- 2024The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey11 Jan 2024
- 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainability27 Oct 2023
- 2023Effectiveness of Moving Target Defenses for Adversarial Attacks in ML-based Malware Detection1 Feb 2023
- 2022RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT30 Dec 2022
- 2022StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection15 Feb 2022
- 2022A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks25 Jan 2022
- 2021Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey21 Jan 2021