基于人工智能的网络入侵检测方法研究

Research on Network Intrusion Detection Methods Based on Artificial Intelligence

随着网络环境的日益复杂和入侵威胁的不断升级,致力于研究一种基于卷积神经网络(Convolutional Neural Networks,CNN)和K-means聚类的网络入侵检测方法。通过构建综合性的网络入侵检测系统架构,利用深度学习和聚类分析相结合的方式,提高对网络流量中入侵行为的敏感性和准确性。在实验阶段,采用1998DARPA数据集进行验证,通过CNN提取特征向量,并应用K-means聚类进行数据分析,实现对网络入侵的有效检测。结果表明,所提方法在准确率、召回率和精确率等方面表现出色,为网络安全领域提供一种可靠的解决方案。

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.