Summary Cloud's operating‐system‐level virtualization has introduced a new phase of lightweight virtualization through containers. The architecture of cloud‐native and microservices‐based application development strongly advocates for the use of containers due to their swift and convenient deployment capabilities. However, the security of applications within containers is important, as malicious or vulnerable content could jeopardize the container and the host system. This vulnerability also extends to neighboring containers and may compromise data integrity and confidentiality. The article focuses on developing an intrusion detection system tailored to containerized cloud environments by identifying system call analysis techniques and also proposes an anomaly‐based host intrusion detection system (Ab‐HIDS). This system employs the frequency of N‐grams system calls as distinctive features. To enhance performance, two ensemble learning models, namely voting‐based ensemble learning and XGBoost ensemble learning, are employed for training and testing the data. The proposed system is evaluated using the Leipzig Intrusion Detection Data Set (LID‐DS), demonstrating substantial performance compared to existing state‐of‐the‐art methods. Ab‐HIDS is validated for class imbalance using the imbalance ratio and synthetic minority over‐sampling technique methods. Our system achieved significant improvements in detection accuracy with 4% increase for the voting‐based ensemble model and 6% increase for the XGBoost ensemble model. Additionally, we observed reductions in the false positive rate by 0.9% and 0.8% for these models, respectively, compared to existing state‐of‐the‐art methods. These results illustrate the potential of our proposed approach in improving security measures within containerized environments.

Cloud computing provides computing resources, platforms, and applications as a service in a flexible, cost-effective, and efficient way. Cloud computing has integrated with industry and many other fields in recent years, which prompted researchers to look into new technologies. Cloud users have moved their applications, data and services to the Cloud storage due to the availability and scalability of Cloud services. Cloud services and applications are provided through the Internet-based on a pay-per-use model. Plenty of security issues are created due to the migration from local to remote computing for both Cloud users and providers. This paper discusses an overview of Cloud computing, as well as a study of security issues at various levels of Cloud computing. The article also provides a complete review of security issues with their existing solutions for a better understanding of specific open research issues.

Abstract Cloud computing allocates virtual resources dynamically on user's demand. The sudden rise of data storage and computation in the cloud computing environment may cause an imbalanced workload distribution. As a result, job completion time will be higher in overloaded servers than the underloaded servers in the same environment. Distributing load fairly in the cloud is a crucial challenge. Traditionally, load balancing is used to distribute the workload among multiple servers to overcome the overloading and underloading of servers. This article presents a novel load balancing approach for cloud computing using improved gray wolf optimization algorithm. We compare our approach with harmony search algorithm, artificial bee colony algorithm, particle swarm optimization, and gray wolf optimization algorithms. Results of simulation are encouraging with improved system performance and fair utilization of resources.