# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques
### Intelligent MTD Techniques
#### 3.2.2 Towards Optimized  
Towards optimization is about finding the most effective and counter-measurable defense for a defense system, given multiple conditional constraints, which include the attacker's possible attack behavior, the system's security, the system's overhead, the system's QoS, the users' experience, etc. The current popular technique is to use machine learning to find the best defense strategy solution for the MTD system facing a known attacker. Developing and evaluating cyber-attack and defense game strategies is a hot research topic. To maximize the impact of MTD, defenders must strategically choose when and what changes to make while considering their systems' characteristics and the activities observed by adversaries. Finding the best strategy for MTD is a significant challenge, especially when facing resourceful and determined adversaries who can react to defenders' actions. The game-theoretic approaches [10], such as the classical game model (static game, signal game), Markov game model, differential game model, and evolutionary game model, can be used to extend MTD techniques due to their flexibility in problem formulations to reflect various scenarios in most domains. Reinforcement learning is a good match for this. Reinforcement learning [78] is a learning process in which the learner must discover which actions yield the most lucrative payoffs without being told what action to take under incomplete information conditions. This process is beneficial for assisting cyber attackers and defenders in playing and choosing the optimal strategy. The study of generalized optimal strategy-solving methods revolves around the following procedures:1. To clarify the attackers' and defenders' knowledge about each other and to build a game model (such as the Stackelberg game based on incomplete information and a zero-sum game or a general-sum game according to the actual situation);
2. To consider different reinforcement learning practical scenarios that can be applied to advance the game process and reach game equilibrium finally, e.g., Bayesian-Stackelberg equilibrium;
3. To select the most efficient sets of strategies and consider them as optimized strategies for the MTD system (as shown in Figure 5). In the actual game of cyber-attack and defense, we need to take many factors [79] into consideration, including but not limited to the following factors shown in Table 6:  
We show the development of research in intelligent MTD solutions step by step with examples of research results in recent years, and summarize them in Table 7.  
* Defensive strategy solutions considering specific types of attacks In the first place, we focused on strategic solutions for specific attacks. As a feasible solution to the routing randomization issue proposed in Section 2.3.4, [PERSON] et al. [27] introduced the Deep Deterministic Policy Gradient (DDPG), a deep reinforcement learning algorithm, into routing randomization against eavesdropping attacks. With the superior performance of DDPG in learning from complex environments and high-dimensional data, they generated randomized routing schemes that met both security and