# A Survey on Moving Target Defense: Intelligently Affordable, Optimized and Self-Adaptive
## 3 Intelligent MTD Techniques
### Intelligent MTD Techniques
#### 3.2.2 Towards Optimized
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Explanations** \\ \hline Actual participants & Is there only one attacker and one defender? Realistic situations are likely to face multiple agents. \\ \hline Rationality profile & Perfect rationality or bounded rationality. \\ \hline Environment & The environment includes knowledge about the opponent. In most cases, the attacker has the advantage in this respect and has much more incomplete information about the defender. \\ \hline Play order & In most cases, the order is leader-follower (Stackelberg game), but there are also cases where cards can be played simultaneously, depending on the actual situation. \\ \hline Available strategies & We cannot exhaust all possible types of attacks in the same game, nor can we deploy all possible MTD mechanisms in the same system. \\ \hline Revenue measurement & For the defender, the revenue design is more complex, including but not limited to system security, system overhead, system QoS, and users’ experience. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Factors commonly considered in GT-MTD. Figure 5: Diagram of optimization for multi-MTD methods. QoS requirements based on real-time network states. Additionally, they utilized a P4 network architecture to deploy randomized routing schemes and achieved finer packet-level granularity for randomized routing. [PERSON] et al. [62] proposed two iterative reinforcement learning algorithms to determine an ideal defense strategy against heart bleed attacks, especially when the information about the attacker is unknown or limited. They used Markov chains and stochastic stability in their algorithms by introducing adaptive, robust reinforcement learning capabilities. They showed that their approach could provide near-optimal defense strategies. How can the defense be adjusted if the attacker's strategy keeps changing? Furthermore, how can the system security and system performance be balanced with limited system resources? [PERSON] et al. in [63] established a cyber-attack and defense game model for specific DDoS attacks and proposed an adaptive strategy for MTD, which adaptively adjusted the defense strategy according to changes in environmental conditions. It also balanced system security and system performance by adjusting parameters to adapt to changes in environmental conditions. Similarly, [PERSON] et al. [64] focused on adversarial machine-learning attacks. Adversarial machine learning has become the latest threat in speech recognition and image recognition, which is becoming accessible to people in their daily lives. The authors proposed a moving target defense approach to defending against adversarial machine learning, but instead of proposing a machine learning algorithm to counter such adversarial algorithms, they proposed a switching scheme between machine learning algorithms to defend against adversarial attacks, which is derived from the Stackelberg equilibrium of the game. [PERSON] et al. [67] proposed a strategic selection method based on a machine learning algorithm to defend against adversarial machine learning. They helped the defender implement multiple learners using a game-theoretic approach, which learned and computed the policy space separately and applied possible dominant policies accordingly. Otherwise, it applied Nash-stable policies to solve the optimal policy finally. Through practical applications, the authors concluded that the method could reduce the effectiveness of adversarial attacks on machine learning attacks. Defensive strategy solutions considering generalized types of attacks  
Next, let us consider generalized attack defense types, i.e., we are vaguer about the specific attack and defense methods and focus further on how to enhance this simulation game to be more generalized for application in the face of real situations [80]. [PERSON] et al.