In recent years, the application of network technology has become popular, and the application of the Internet has occupied a large proportion in people’s daily life. The issue of network security has also begun to be paid attention to. In recent years, due to the rapid expansion of network applications, malicious events such as network threats have occurred from time to time. Many computers do not have a good protection structure so that they are often vulnerable to attacks during network interconnection. Traditional computer protection measures are border-based protection, but with the development of network technology, this protection system can no longer guarantee computer security. Therefore, in order to keep computers safe in the current network environment, the role of artificial intelligence in the computer field should be given full play. The artificial intelligence analysis system can analyze and predict the situation of computer network security based on the situation of network security. This paper integrates convolutional neural network algorithms on the basis of traditional machine learning to establish a new network intrusion model. This paper verifies the feasibility of the model through experiments, and the experimental results show that the accuracy of the new model proposed in this paper can reach more than 90% for KDDCUP99 data detection. In addition, traditional computer protection systems have many errors when dealing with DNN attack detection. In order to reduce the occurrence of this situation, this paper proposes a standardized attack detection model based on deep nerves. The detection precision of this model is higher and the results obtained are more accurate. In addition, this new model can also synthesize the impact of different network attacks on the security situation, and construct attack situation predictions for computer systems.
Research on Computer Network Security Analysis Modeling Based on Artificial Intelligence Technology