Sensing network security prevention measures of BIM smart operation and maintenance system

With the continuous expansion of network scale and the increasing complexity of attack methods, traditional network security protection equipment has been unable to cope with large-scale network security detection and protection. However, most current operation and maintenance systems cannot reasonably evaluate and predict network security. In order to be able to evaluate and prevent the network security of the bridge BIM intelligent operation and maintenance system under the background of big data, this paper uses the KDD Cup99 data set and the network attack data in the bridge BIM network environment to simulate the method proposed in this paper. The comparison results verify that the network security risk perception method proposed in this paper can realize network security risk perception more accurately and efficiently. This paper proposes a data mining method based on Bayesian network algorithm to evaluate the risk value of the bridge BIM intelligent operation and maintenance system. During the period from 0 to 110 min, the network risk value increased from 0.003 to 0.91. It can be seen that with the deepening of the attack phase, the degree of network risk will also increase. This paper uses the detection rate, false negative rate, false positive rate, AUC and other indicators to conduct simulation experiments on the data prediction-based network security risk prediction algorithm and comparison algorithm proposed in this paper. Simulation experiments show that under the four simulation experiment environments (pl = pe = 0.01/0.15, n = 20/50), the AUC of this scheme is increased by 0.018, 0.053, 0.008 and 0.11, respectively. The proposed algorithms are better than the comparison algorithms.