CCNN-based digital awareness strategy of data network security situation

Aiming at the problem that the data Network is under threat attack and the situation element extraction is insufficient, which leads to the problem that the security situation awareness results are inconsistent with the actual situation, a digital awareness method of data network security situation based on Composite Convolutional Neural Network (CCNN) is proposed. Combined with CCNN technology, the composite series structure unit is constructed. The distributed time-frequency response of data network security situation was calculated, and the data fusion result of data network security situation was obtained. The final weight of the index layer is obtained by inputting the weight of the evaluation to be classified, calculating the Euclidean distance between each feature and the cluster family. The attack success coefficient was calculated by combining the attack force and the vulnerability of the attack target host, and it was used as the judgment standard to realize the digital awareness of network security situation. The experimental results show that the maximum security situation values obtained by this method under illegal access and remote unauthorized access are 0.51 and 0.25 respectively, and there is only a maximum error of 0.01 with the actual value, which can accurately perceive the network security situation.