基于深度学习的网络入侵检测与防御机制

Network intrusion detection and defense mechanism based on deep learning

该研究旨在探索基于深度学习的网络入侵检测与防御机制,以卷积神经网络(Convolutional Neural Network,CNN)算法和CSE-CICIDS2018数据集为基础,通过综述传统网络入侵检测方法和深度学习在网络安全领域的应用,分析了当前研究的发展状况和存在的问题。实验过程选用了CNN算法作为主要的深度学习模型,并设计了相应的网络架构。通过对CSE-CICIDS2018数据集的实验评估,研究发现基于CNN算法的网络入侵检测与防御机制在识别异常流量和正常流量方面表现出良好的性能。该研究为进一步提升网络安全水平和效率提供了可行的方案,并为未来相关研究提供了借鉴和展望。

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.