Intrusion Detection in Cloud Computing Virtualization Using Radial Basis Function Neural Network Optimized by Grasshopper Optimization Algorithm

Cloud computing plays a crucial role in handling massive computations, providing a very simple computational model for users that meets their requests and needs at minimal cost. One of the major challenges in using cloud computing infrastructure is data security and preventing various possible intrusions. Intrusion detection systems (IDS) are one of the main components of cloud computing environment monitoring systems. This paper presents a hybrid learning system for use in intrusion detection systems in virtualization within cloud computing. After data collection and preparation, a Radial Basis Function Neural Network (RBFNN) trained with the Grasshopper Optimization Algorithm (GOA) is used as the proposed method for intrusion detection in cloud computing virtualization. GOA is employed to determine the centers, spread parameters, and weights of neurons in the RBFNN. The results are compared with the k-Nearest Neighbor (k-NN) classifier based on various error types and standard performance criteria. Simulation results indicate a ۹۶.۳% accuracy for the proposed method and show superior performance.

کلیدواژه ها:

Intrusion Detection ، virtualization ، cloud computing ، Radial Basis Function Neural Network ، Grasshopper Optimization Algorithm