# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques
### Discussion  
Machine learning-based MTD allows systems to capture evolving attack patterns with high scalability and applicability and has become the mainstream research direction for current MTD techniques. With the guideline of complementing the shortcomings of traditional MTD techniques, we divided the research direction of intelligent MTD into three major points: _towards affordable, towards optimized,_ and _towards self-adaptive._ Among them, towards self-adaptive is the most advanced and effective technology orientation for MTD and shares the features of towards affordable and towards optimized. Combined with the survey, we demonstrated the following main points with Table 9.  
* When introducing machine learning to address existing MTD problems, it is important to ensure the two fit together seamlessly. Achieving optimal results requires a rigorous validation process. Machine learning often requires large amounts of data for training, which can introduce additional overhead and complexity. In addition, many machine learning algorithms rely on high-performance GPU computing cards and occupy significant storage space. In addition, inefficient machine learning algorithms can also pose processing efficiency issues, so sufficient computing power must be provided in the environment where MTD is deployed.
* In addition, existing intelligent MTD research has focused on optimized solutions that specify attack defense types, and these have shown promising results in real-world cyber-attack and defense scenarios. However, they are only applicable to specific scenarios and may be limited in practical situations where attack defense types change rapidly. In contrast, generalized attack types can model the real world more closely and take more factors into account. However, when information about rational adversaries is incomplete, these models may yield sub-optimal strategies in sequential settings. Furthermore, existing efforts to learn defense policies in sequential settings are either unpopular or neglect the strategic nature of the adversary due to scalability issues caused by incomplete information.
* At last, self-adaptive MTD solves various problems caused by manual decision-making regarding MTD trigger intervals and balances security and resource costs. It can also extract features for optimal defense strategy selection in the face of new attack methods. However, the design of the engine for sensing attack behavior and analyzing attack features is complicated. The continuous collection of data samples is required to sense attacks, and the adaptive effect heavily depends on the algorithm and sensing engine.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref.** & **ML Method** & **Taxonomy** & **Affordability** & **Optimization** \\ \hline [PERSON] et al. [69] & DL & - & \(\surd\) \\ [PERSON] et al. [19] & CNN & ML-based & \(\surd\) & \(\surd\) \\ [PERSON] et al. [14] & Neuro-evolution & ML + defense & \(\surd\) & - \\ [PERSON] et al. [48] & Clustering & mechanism-based & - & \(\surd\) \\ [PERSON] et al. [36] & RL & - & \(\surd\) \\ [PERSON] et al. [70] & DNN & ML + GT-based & - & \(\surd\) \\ \hline \end{tabular}
\end{table}
Table 8: Our survey on recent MTD self-adaptation researches.