Research on Computer Network Security Protection Technology Incorporating Full Convolutional Networks

To strengthen networks’ security performance, here pose suggests a fused full convolutional approach to monitoring computer networks. This paper first analyzes the various performances of the full convolutional model for error problems rate, error reporting, and monitoring effects on various attack categories then proposes a network monitoring scheme for the full convolutional model and introduces the workflow of the full convolutional model in computer network security protection. In terms of accuracy, the full convolutional error rate of the blueprint meets the requirements rate of 96.8%, which is better than the classical network models of Lenet-5 and AlexNet, with 86.2% and 91.6%. The false alarm rate is only 2.37%, which is lower than the 5.74% MLP algorithm and 4.23% SVM algorithm. By comparison, the full convolutional calculation method is more efficient than other calculation methods in the detection rate of attack types such as Dos, Probe, U2R, and R2L. Therefore, the calculation method here is well adapted to computer network security protection requirements.