Threat Attribution and Reasoning for Industrial Control System Asset

Abstract

Due to the widespread use of the industrial internet of things, the industrial control system has steadily transformed into an intelligent and informational one. To increase the industrial control system's security, based on industrial control system assets, this paper provides a method of threat modeling, attributing, and reasoning. First, this method characterizes the asset threat of an industrial control system by constructing an asset security ontology based on the asset structure. Second, this approach makes use of machine learning to identify assets and attribute the attacker's attack path. Subsequently, inference rules are devised to replicate the attacker's attack path, thereby reducing the response time of security personnel to threats and strengthening the semantic relationship between asset security within industrial control systems. Finally, the process is used in the simulation environment and real case scenario based on the power grid, where the assets and attacks are mapped. The actual attack path is deduced, and it demonstrates the approach's effectiveness.