A major development in the field of access control is the dominant role-based access control (RBAC) scheme. The fascination of RBAC lies in its enhanced security along with the concept of roles. In addition, attribute-based access control (ABAC) is added to the access control models, which is famous for its dynamic behavior. Separation of duty (SOD) is used for enforcing least privilege concept in RBAC and ABAC. Moreover, SOD is a powerful tool that is used to protect an organization from internal security attacks and threats. Different problems have been found in the implementation of SOD at the role level. This paper discusses that the implementation of SOD on the level of roles is not a good option. Therefore, this paper proposes a hybrid access control model to implement SOD on the basis of permissions. The first part of the proposed model is based on the addition of attributes with dynamic characteristics in the RBAC model, whereas the second part of the model implements the permission-based SOD in dynamic RBAC model. Moreover, in comparison with previous models, performance and feature analysis are performed to show the strength of dynamic RBAC model. This model improves the performance of the RBAC model in terms of time, dynamicity, and automatic permissions and roles assignment. At the same time, this model also reduces the administrator’s load and provides a flexible, dynamic, and secure access control model.

No abstract

To meet the increasing demand for its services, a cloud system should make optimum use of its available resources. Additionally, the high and low oscillations in cloud workload are another significant symmetrical issue that necessitates consideration. A suggested particle swarm optimization (PSO)-based ensemble meta-learning workload forecasting approach uses base models and the PSO-optimized weights of their network inputs. The proposed model employs a blended ensemble learning strategy to merge three recurrent neural networks (RNNs), followed by a dense neural network layer. The CPU utilization of GWA-T-12 and PlanetLab traces is used to assess the method’s efficacy. In terms of RMSE, the approach is compared to the LSTM, GRU, and BiLSTM sub-models.

No abstract

No abstract