文章综合运用大数据聚类技术和深度学习方法,提出一种基于密度的空间聚类算法Density-Based Spatial Clustering of Applications with Noise,DBSCAN)、K-means聚类以及长短期记忆(Long Short-Term Memory,LSTM)神经网络的通信网络安全态势预测方法.该方法通过聚类分析多源异构的网络安全数据,提取关键安全态势特征,并利用LSTM模型建立安全态势预测模型.实验结果验证了该方法的有效性,为智能化网络安全管理提供新的思路.
Research on the Situation Pprediction Method of Communication Network Security Based on Big Data Clustering
The article synthesizes big data clustering technology and deep learning methods to propose a density-based spatial clustering algorithm Density-Based Spatial Clustering of Applications with Noise(DBSCAN),K-means clustering,and Long Short-Term Memory(LSTM)neural network for communication network security posture prediction method.The method analyzes multi-source heterogeneous network security data by clustering,extracts key security posture features,and establishes a security posture prediction model using the LSTM model.The experimental results verify the effectiveness of the method and provide new ideas for intelligent network security management.
network security posturebig data clusteringLong Short-Term Memory(LSTM)networks
万方数据
维普
