Blended threat prediction based on knowledge graph embedding in the IoBE

Owing to the hyper-connectivity of convergence environments, the Internet of Blended Environments (IoBE) has emerged As a result, the environments and architectures in which cyber-security threats can occur have steadily diversified leading to an increase in security incidents. However, existing detection systems lack correlation analysis and thus cannot detect the corresponding diverse attack paths and attack chains effectively. In this paper, we propose a data prediction technique in which knowledge graph embedding technology is applied to predict blended threats in complex environments such as the IoBE. We also verify the performance of the proposed technique.