## 4 Datasets  
Due to the abundance of parameters and the risk of overfitting, the training of deep learning models often requires large-scale datasets. To the best of our knowledge, there are currently a few datasets for system call anomaly detection based on containerized environments, among which the two most suitable are the LID-DS [34] and CB-DS [20]. However, to validate the effectiveness of the proposed method in a modern container cloud microservice environment closer to the real world, we constructed a scenario based on a sample of Spring Cloud on Kubernetes. The service structure is depicted in Figure 6.  
In our discussion, we will approach the container security challenge from two perspectives. First, the security of applications running in containers. If an application has vulnerabilities, running it in a container cannot prevent these vulnerabilities from being exploited. Second, we consider the security of the containerization mechanism itself. Ensuring that the isolation and restriction of the runtime environment produce a reliable system. Otherwise, when containers are run in a multi-tenant environment, they may suffer a loss of confidentiality due to data leakage. We assume that the attacker originates from external sources and has not yet gained any initial access privileges to the system, such as having an account within the cloud environment. We do not consider denial of service (DoS) attacks, as well as attacks targeting the operating system and layers below, such as hardware vulnerabilities, as these types of attacks are not specific to the containerized cloud environment or containerized applications. We also do not consider the prevention  
Figure 6: Scenario struct. or tampering of captured system calls by the attacker after achieving privilege escalation. Such behavior should be blocked upon the detection of privilege escalation. Under normal scenarios, external users access the Gateway Service in the microservices at random through different containers via WebUI with a concurrency ranging from 1 to 1000 requests per second. The requests are then processed by the Organization Service, Department Service, and Employee Service, and interactions occur with the Redis and MySQL databases on the backend. In typical access patterns, operations include adding, deleting, updating, and querying employee, organization, and department data, with each operation randomly executed according to a specified range of proportions. While under abnormal scenarios, there are ten different types of anomalous access, as described in detail in Table 2. To simulate attacks based on application vulnerabilities, we configured the Spring Boot version as 2.6.3, Spring Cloud version as 2021.0.0, Spring Cloud Gateway version as 3.0.6, and MySQL version as 5.23. We also exposed the Gateway Actuator endpoints to exploit Common Vulnerabilities and Exposures(CVE) vulnerabilities for Remote Code Execution (RCE) and Arbitrary File Access (AFA). To simulate the misconfiguration exploits, we mount the docker.sock and a host path (/etc) to the target pod, then the attacker uses the CVE_2022_22965/CVE_2022_22947/CVE_2022_0543 RCEs to achieve further exploits. To remove the trace of CVE exploits, the further exploits are delayed by several seconds, and the actual starting timestamps are recorded in the labels. With the above approach, we constructed the dataset with the following characteristics: **Environment realism**: Real container cloud scenarios are simulated by Spring on the Kubernetes framework, with reasonable access as normal behavior. **Accurate labeling**: Unlike the way of dividing by whole segments, we label the occurrence of attacks by timestamps, which makes the validation of the effect of anomaly detection more accurate. **Reduced similarity**: Through multi-container, multi-environment deployment, the dataset itself is less redundant and has better robustness. **Scalability and reproducibility**: Through modularized Skaffold configuration, it makes subsequent scenarios increase and reproduction possible.