Network intrusion detection and defense mechanism based on deep learning
This study aims to explore network intrusion detection and defense mechanism based on deep learning,using the CNN algorithm and the CSE-CICIDS2018 dataset as the foundation.By reviewing traditional network intrusion detection method and the application of deep learning in the field of network security,the current research status and identify existing issues are analyzed.In the experiments,the CNN algorithm is selected as the main deep learning model,and the corresponding network architecture is designed.Through the evaluation experiments on the CSE-CICIDS2018 dataset,it is found that the network intrusion detection and defense mechanism based on the CNN algorithm demonstrate good performance in identifying anomalous traffic and normal traffic.This study provides the feasible solutions for the further improving network security levels and efficiency,and offers insights for future related research.
deep learningnetwork intrusion detectiondefense mechanismCNN algorithmCSE-CICIDS2018 dataset
NETL
NSTL
万方数据
