A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive

Represented by reactive security defense mechanisms, cyber defense possesses a static, reactive, and deterministic nature, with overwhelmingly high costs to defend against ever-changing attackers. To change this situation, researchers have proposed moving target defense (MTD), which introduces the concept of an attack surface to define cyber defense in a brand-new manner, aiming to provide a dynamic, continuous, and proactive defense mechanism. With the increasing use of machine learning in networking, researchers have discovered that MTD techniques based on machine learning can provide omni-bearing defense capabilities and reduce defense costs at multiple levels. However, research in this area remains incomplete and fragmented, and significant progress is yet to be made in constructing a defense mechanism that is both robust and available. Therefore, we conducted a comprehensive survey on MTD research, summarizing the background, design mechanisms, and shortcomings of MTD, as well as relevant features of intelligent MTD that are designed to overcome these limitations. We aim to provide researchers seeking the future development of MTD with insight into building an intelligently affordable, optimized, and self-adaptive defense mechanism.

Automated summary

Represented by reactive security defense mechanisms, cyber defense is currently static, reactive, and costly. Moving target defense (MTD) introduces a new approach with the goal of providing dynamic, continuous, and proactive defense. MTD techniques leveraging machine learning have shown promise in reducing costs and providing effective defense capabilities. However, research in this area is fragmented and more progress is needed to build a robust and available defense mechanism. Therefore, we conducted a comprehensive survey to provide insight into the design and limitations of MTD, as well as intelligent MTD features to overcome these limitations.