A Two-level Optimized Genetic Algorithm for Adaptive Warning

In network data exchange scenario, potential network security risks may appear inadvertently, different physical nodes have different levels of risk warning requests, and the cost of security risk warning are not identical. Therefore, the warning mechanism has become a key issue for the secure exchange of data in the network environment. In this paper, the mathematical model of warning mechanism is first established. The association between network nodes, security risks and warning components are accordingly established, and a Two-level Optimization Genetic Algorithm (TOGA) is proposed to find the best warning scheme with the lowest warning cost through iteration. Experimental results show the superiority of the proposed algorithm.