Search

To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.

Attack test and defense verification are important ways to effectively evaluate the cybersecurity performance of Intelligent Connected Vehicle (ICV). This paper investigates the problem of attack-defense visualization in ICV cybersecurity. For the purpose of promoting cybersecurity research capabilities, a novel Cybersecurity Attack-Defense Visualization method based on Intelligent Connected Vehicle (CADV-ICV) is proposed. In this scheme, an Attack-Defense Game model (ADG) is designed so that the logical relationship between the attack and defense can be studied through a system architecture. Then, the CADV-ICV method is implemented through three layers that are hardware layer, software layer and visualization layer. Finally, through an Intelligent Connected Vehicle, two TV monitors, a computer and a server, a real experimental environment is built to test the CADV-ICV method. The experimental results show that CADV-ICV can realize the visual display of attack-defense process, attack messages, defense state, real-time message monitoring, and attack-defense principle for 10 car’s components.