Simulation of Cloud Computing Network Security Intrusion Detection Model Based on Convolutional Neural Network

Due to the rapid growth of Internet technology, computers have been integrated into all aspects of human life, which has brought great convenience to people’s lives. However, the scale of network data has become larger and larger, and many network threats have impacted network security, causing huge security risks. In this paper, we use the data features of Convolutional Neural Network (CNN) to extract the technical features of autonomous discovery and extraction with high accuracy, and propose an intrusion detection system (IDS) model based on CNN algorithm. The preprocessed training set data is used as input data to propagate forward and extract network features, and the output data of cross-layer aggregation network is merged; Then, according to the classification results, the training error is calculated and iteratively optimized through the back propagation process until the model converges; Finally, the trained classifier is used to classify the test data set. The test results of public data sets show that the model has higher accuracy and lower false negative rate than traditional intrusion detection methods.