부산대학교 도서관

Trust assessment is a crucial parameter in the next generation WCN (Wireless Communication Network) for the secure collaboration of nodes with each other and the detection of the possible security menaces in the network. The trust ranking of nodes especially in the platforms of defense networks and healthcare networks is of vital importance to the security threats inside the network. In this paper, we have suggested an RM dataset based on the current possible attacks along with advanced authentication attributes for the next generation WCN (B5G/6G). Further, we proposed a trust-ranking model based on the SVM machine learning technique to define the trust ranks of the users present in the network. The model consists of five trust rankings. The fifth trust rank is defined as the highest level of trust and the first trust rank designates the lowest level of trust. The attained rank of trust determines the specific services offered to the corresponding node. The proposed prediction model based on the SVM (Support Vector Machine) algorithm maintains the triple-off balance between the computational time, accuracy, and security. The simulation results indicate that the proposed SVM-based trust ranking model intelligently identifies possible malicious threats and provides an advancement in network security.