A Survey of Moving Target Defenses for Network Security
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Abstract (arXiv)
Network defenses based on traditional tools, techniques, and procedures fail to account for the attacker's inherent advantage present due to the static nature of network services and configurations. To take away this asymmetric advantage, Moving Target Defense (MTD) continuously shifts the configuration of the underlying system, in turn reducing the success rate of cyberattacks. In this survey, we analyze the recent advancements made in the development of MTDs and define categorizations that capture the key aspects of such defenses. We first categorize these defenses into different sub-classes depending on what they move, when they move and how they move. In trying to answer the latter question, we showcase the use of domain knowledge and game-theoretic modeling can help the defender come up with effective and efficient movement strategies. Second, to understand the practicality of these defense methods, we discuss how various MTDs have been implemented and find that networking technologies such as Software Defined Networking and Network Function Virtualization act as key enablers for implementing these dynamic defenses. We then briefly highlight MTD test-beds and case-studies to aid readers who want to examine or deploy existing MTD techniques. Third, our survey categorizes proposed MTDs based on the qualitative and quantitative metrics they utilize to evaluate their effectiveness in terms of security and performance. We use well-defined metrics such as risk analysis and performance costs for qualitative evaluation and metrics based on Confidentiality, Integrity, Availability (CIA), attack representation, QoS impact, and targeted threat models for quantitative evaluation. Finally, we show that our categorization of MTDs is effective in identifying novel research areas and highlight directions for future research.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 1; 2026: 1.
5 papers.
- 2022Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanismscited by 4
- 2024DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customizationcited by 2
- 2025Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaversescited by 2
- 2024Preserving the Privacy of Reward Functions in MDPs through Deceptioncited by 1
- 2026Aggressive Compression Enables LLM Weight Theftcited by 1
- 2026Aggressive Compression Enables LLM Weight Theft3 Jan 2026
- 2025Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses19 May 2025
- 2024DYNASHIELD: A Black-Box Moving Target Defense for LLMs via Dynamic Decoding Customization10 Dec 2024
- 2024Preserving the Privacy of Reward Functions in MDPs through Deception13 Jul 2024
- 2022Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms5 Jul 2022