## 1 Introduction  
Container technology is now one of the mainstream technologies in the field of cloud computing [1]. In contrast to the traditional virtual machine (VM) model, containers share the same kernel with the host, making them more efficient in terms of resource sharing, deployment, and scalability. This makes them well-suited to microservices architectures, serverless applications, and CI/CD (continuous integration and continuous development) workflows. The container ecosystem has been widely adopted on major cloud-computing platforms. However, container technology does not inherently solve the security issues found in traditional edge-cloud environments. In fact, due to lower resource isolation and the dynamic relationship between microservices across distributed edge nodes, containerized edge clouds face more security risks, such as container escape through kernel vulnerabilities, impacting the confidentiality, integrity, and availability of the host and cloud platform. The CNCF 2022 survey [2] indicated that 41% of respondents considered container security issues a significant challenge in production environments. On the other hand, the widespread adoption of microservices architectures has led to an expanded attack surface and increased risks. In contrast to monolithic applications with few entry points, microservices-based edge applications have numerous entry points, all of which must be protected. In the past, people relied on simply trusting the network in order to avoid security checks for each microservice. However, currently, network trust has become an outdated approach, and the industry is moving towards the principles of a zero-trust network. The polyglot architecture also increases the cost of securing microservices, as each technology stack requires its own security tools for static code analysis and dynamic testing. While Linux provides various kernel security mechanisms like Namespace, Cgroup, and Capability [3] as the foundation for container environments, research [4] suggests that these mechanisms are not sufficient to defend against all privilege escalation vulnerabilities. Furthermore, the interdependencies and interactions among these kernel security mechanisms can weaken their protective capabilities. Consequently, organizations have increasingly adopted Kubernetes security tools, with observability tools being the most prominent, to address these security concerns, including security tools based on system calls. System calls, as the interface for interactions between user programs and the operating system, provide valuable insights into various system resource-related behaviors, such as device I/O (input/output) operations or network communication. Thanks to their advantages of lightweight operation and low latency, system calls have emerged as critical data sources for anomaly detection in containerized edge-cloud environments. Due to the shared kernel nature of containers (except for certain \"secure containers\", like Kata ([[https://katacontainers.io/](https://katacontainers.io/)]([https://katacontainers.io/](https://katacontainers.io/)))), system call behaviors can easily be obtained in a non-proxy mode on the host, making them one of the mainstream approaches for container security monitoring. Currently, the primary methods for detecting container system call anomalies include rule-based and machine learning-based approaches. **Rule-based methods** face the challenge of effectively generating rules in practice. Additionally, such methods often only apply to individual anomalies and are unable to detect anomalies at the sequence level. **Methods based on machine learning** face the challenges of performance overheads and false alarms. It has been observed that a single container application can generate millions of system calls per second during normal operation, and the number of system calls can only be larger in containerized edge-cloud environments.