# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 4 Conclusions
### Future Research  
* We propose the establishment of a more comprehensive coverage of MTD classifications in future work. In our survey, we introduced the SDR/Five layers MTD classification by what to move and how to move, but we did not combine it with when to move to cover more comprehensive MTD techniques. It is necessary to develop a classification that can comprehensively include multidimensional MTD attributes to help researchers better understand and develop MTD techniques.
* Security, performability, and affordability are important indicators when measuring the effectiveness of MTD techniques. While MTD improves system security, it may also hinder service availability to average users. In future research, we suggest exploring finer granularity to develop affordable MTD solutions that meet the average users' needs. Notably, most existing MTD approaches do not provide highly lightweight distributed solutions. Therefore, we recommend building more lightweight MTD techniques in the future to enable the higher widespread deployment and application of MTDs.
* Comprehending the attacker's behavior or system security situation is crucial in enabling defenders to make optimal decisions. However, there are many factors to consider for practical application, and extrapolation can easily lead to sub-optimal strategies. To address this, MTD should explore a wider range of practical scenarios with reinforcement learning to help defenders solve more difficult adversaries.
* We believe that the concepts and techniques of self-adaptive MTD are not yet mature, and therefore, more self-adaptive MTD mechanisms need to be developed. In terms of triggering MTD operations, we need to balance the bi-directional costs of triggering and security by considering factors such as system vulnerability or attack pattern/strength. This requires advanced detection or learning capabilities from defenders.
* The advancement of intelligent MTD techniques cannot be achieved without the support of big data and high-performance hardware. Therefore, future intelligent MTD researchers should consider constructing larger sample datasets and forming systematic sample databases for training machine learning algorithms and improving accuracy and generalization ability. Using hardware devices with higher computing performance can also enhance training efficiency and the real-time decision-making of MTD.
* Deceptive defense [83] ideas have emerged in recent years, and combining deceptive defense methods with MTD could be a future research trend. There is already a trend of combining deceptive defense with intelligence [81], where deceptive defense provides misleading information by actively exposing false intelligence of the protected system, thus allowing the attacker to move the attack in the direction of favoring the defender by actively creating and reinforcing the observation and identification of deceptive information.  
Conceptualization, R.S. and Y.Z.; methodology, R.S. and J.F.; investigation, R.S. and X.C.; writing--original draft preparation, R.S.; writing--review and editing, R.S., Y.Z. and J.F.; supervision, J.F.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the Key JCJQ Program of China, grant number 2020-JCJQ-ZD-021-00 and 2020-JCJQ-ZD-024-12. The authors would like to acknowledge them.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No new data were created.