A Network Attack Risk Control Framework for Large-Scale Network Topology Driven by Node Importance Assessment

In large-scale network scenarios, network security data are characterized by complex association and redundancy, forming network security big data, which makes network security attack and defense more complicated. In this paper, the authors propose a framework for network attack risk control in large-scale network topology, called NARC. Using NARC, a user can determine the influence level of different nodes on the diffusion of attack risk in complex network topology, thus giving optimal risk control decisions. Specifically, this paper designs a topology-oriented node importance assessment model, combined with node vulnerability correlation analysis, to construct a diffusion network of attack risks for identifying potential attack paths. Furthermore, the optimal risk control node selection method based on game theory is proposed to obtain the optimal set of defense nodes. The experimental results demonstrate the feasibility of the proposed NARC, which helps to ease the risk of network attacks.