Research on Network Intrusion Detection Methods Based on Artificial Intelligence
As the network environment becomes increasingly complex and intrusion threats continue to escalate,this article is dedicated to studying a network intrusion detection method based on Convolutional Neural Network(CNN)and K-means clustering.By building a comprehensive network intrusion detection system architecture and using a combination of deep learning and cluster analysis,the sensitivity and accuracy of intrusion behaviors in network traffic are improved.In the experimental stage,this study used the 1998 DARPA data set for verification,extracted feature vectors through CNN and applied K-means clustering for data analysis,achieving effective detection of network intrusions.The results show that the proposed method performs well in terms of accuracy,recall and precision,providing a reliable solution in the field of network security.
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