Biblio

{With the rapid development of cyberspace, cyber security incidents are increasing, and the means and types of network attacks are becoming more and more complex and refined, which brings greater challenges to security risk control. First, the knowledge graph technology is used to construct a cyber security knowledge graph based on ontology to realize multi-source heterogeneous security big data fusion calculation, and accurately express the complex correlation between different security entities. Furthermore, for cyber security risk control, a key node assessment method for security risk diffusion is proposed. From the perspectives of node communication correlation and topological level, the calculation method of node communication importance based on improved PageRank Algorithm and based on the improved K-shell Algorithm calculates the importance of node topology are studied, and then organically combine the two calculation methods to calculate the importance of different nodes in security risk defense. Experiments show that this method can evaluate the importance of nodes more accurately than the PageRank algorithm and the K-shell algorithm.

For the occurrence of network attacks, the most important thing for network security managers is how to conduct attack security defenses under low-risk control. And in the attack risk control, the first and most important step is to choose the defense node of risk control. In this paper, aiming to solve the problem of network attack security risk control under complex networks, we propose a game attack risk control node selection method based on game theory. The method utilizes the relationship between the vulnerabilities and analyzes the vulnerability intent information of the complex network to construct an attack risk diffusion network. In order to truly reflect the different meanings of each node in the attack risk diffusion network for attack and defense, this paper uses the host vulnerability attack and defense income evaluation calculation to give each node in the network its offensive and defensive income. According to the above-mentioned attack risk spread network of offensive and defensive gains, this paper combines game theory and maximum benefit ideas to select the best Top defense node information. In this paper, The method proposed in this paper can be used to select network security risk control nodes on complex networks, which can help network security managers to play a good auxiliary role in cyber attack defense.