# Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing
## 2 Related Works  
Edge computing has received much attention in recent years. Several studies have focused on the security issues in edge computing and provided some defense mechanisms. For instance, [PERSON] et al. [15] summarized the challenge of the security issues of Internet of Things (IoT) edge devices. The focus of this work was mainly on the classifications of attacks and threats for the devices with limited resources and a discussion of the defense strategies at different edge network layers for different security threats. In [16], a study was conducted to focus on the deployment of defense mechanisms to address security issues in edge computing. In this work [16], the security issues in edge computing systems were categorized into the perception layer, network layer, and application layer, and then the defense problem was analyzed from the perspective of artificial intelligence.  
[PERSON] et al. [17] introduced a cooperative defense framework for defending against DDoS attacks in mobile edge computing, which could adapt to traffic changes by automatically coordinating container-carrying defense resources among the edge nodes. [PERSON] et al. [18] proposed a distributed deep defense framework by using edge computing approaches, which could detect and mitigate DDoS attacks near the data source; this defense framework could significantly reduce unnecessary bandwidth consumed by DDoS traffic going from edge network to edge network. [PERSON] et al. [19] proposed a layered approach to research the different categories of denial of service (DoS) and distributed denial of service (DDoS) attacks in edge computing systems. They analyzed the inherent vulnerabilities and weaknesses of attacks and proposed an architecture with detection and defense mechanisms based on federated learning. [PERSON] et al. [20] proposed a new defense framework in edge computing scenarios for the prediction and detection of DDoS attacks.  
[PERSON] et al. [21] introduced an eavesdropping-based attack-aware cache defense algorithm that could mitigate the effects of the attacker on the caching performance. [PERSON] et al. [22] proposed a defensive quantization method to mitigate the perturbations from the malicious samples in edge computing. Since improving the defense level means occupying more additional computation resources, the authors of [23] discussed the tradeoff between limited resource optimization and defense level improvement in edge computing offloading. Moreover, game theory has been used to solve the problem of resource-constrained resources and the security defense level. The survey in [24; 25] summarized the methods that have been adopted to solve attacker-defender games and found that the current attacker-defender games that focus on technology adoption assume that the defender will deploy a single new technology at all target sites. The work discussed the future trends and research directions for applying game theory models in edge services and considering usage scenarios. For edge DDoS attacks, [26] proposed a novel game-theoretical approach named EDM Game and obtained the Nash equilibrium by using a decentralized algorithm. [PERSON] et al. [27] analyzed the gains of defense mechanisms based on the stochastic differential game theory. [PERSON] et al. [28] modeled the interaction behavior between defenders and attackers as a stochastic game model for resource-constrained devices and determined the optimal defense strategy. [PERSON] et al. [29] proposed a mean-field game model to solve the data security issues in edge computing.  
Although several works have considered the balance between limited resource consumption and security, some schemes achieve this goal by designing specific detection and defense technology, while others achieve this through game models. Few works consider the coupling relationship between the objective of attackers and the strategy of each defender and the dynamic changes in defense decisions under constrained resource consumption.