Mining of Potential Relationships based on the Knowledge Graph of Industrial Control Systems

Abstract

Industrial Control System(ICS) security is one of the lifebloods of national development. Fully understanding of its vulnerabilities plays an important role in the actual application scenarios. Meanwhile, an attacker may also exploit multiple vulnerabilities to achieve the final malicious purpose, such as the Stuxnet worm. In order to solve the above problems, we construct a Knowledge Graph(KG) of heterogeneous ICSs, and propose a potential relationship mining method (R-HetGNN) based on this graph. The method solves the multi-modality problem in KG aggregation and KG-heterogeneity problem. Besides, we use random walk algorithm to solve the ulti-level neighbor problem. Experimental results on a real-world dataset show that R-HetGNN achieved 83.0% on the F1 score, superior to other knowledge reasoning modules, such as GAT and TransE.