Research on Network Intrusion Detection Method Based on CNN

A CNN based network intrusion detection system is proposed to address the increasing complexity and variability of current network attack patterns, which pose challenges for timely warning. The aim is to provide a secure environment for the use of large-scale data. Using the KDD cup99 dataset as the experimental dataset, the data is first subjected to single hot encoding and normalization to ensure consistency and accuracy. Then, the Random Forest algorithm is used for feature selection to select the most representative features. Finally, the processed data is fed into the constructed CNN model for classification to accurately identify the type of network attack. The experimental results show that this method can monitor network traffic in real time, accurately analyze abnormal behaviors and attack characteristics in the data, and quickly and accurately identify the types of network attacks.