An Ontology-Based Dynamic Attack Graph Generation Approach for the Internet of Vehicles - Hou, Shuning; Chen, Xiuzhen; Ma, Jin; Zhou, Zhihong; Yu, Haiyang (Frontiers in Energy Research, 2022)
With the development of automobile intelligence, the security of the Internet of Vehicles has become a key factor that affects the development of intelligent vehicles. However, existing security risk analysis methods for the IoV either focus only on certain levels, such as the component level, or perform only a static analysis. This paper proposes a dynamic attack graph generation method for the IoV to identify and visually display the security risks caused by the associated vulnerabilities in an IoV system. First, using the actual architecture of the IoV, this paper shows how to model the security elements and their relationships in the IoV system and proposes a network security ontology model for this system. Second, it shows how to construct a reasoning rule base according to the causal relationship between the vulnerabilities using the Semantic Web Rule Language Finally, in view of the rapid change in the network topology of the IoV, a dynamic attack graph generation algorithm based on an ontology reasoning engine is proposed, which can effectively reduce the overhead caused by the changes in the attack graph. The effectiveness of the algorithm is demonstrated through an actual security event scenario and a constructed scenario. The experimental results show that the algorithm can dynamically and accurately display the network attack graph of the IoV. The proposed method is helpful in globally analyzing the threat caused by the combined exploitation of the vulnerabilities in an IoV system and risk management.