Research on the Situation Pprediction Method of Communication Network Security Based on Big Data Clustering
马馥宇1
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作者信息
1. 沈阳城市学院,辽宁 沈阳 110000
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摘要
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.
Abstract
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.
关键词
网络安全态势/大数据聚类/长短期记忆(LSTM)网络
Key words
network security posture/big data clustering/Long Short-Term Memory(LSTM)networks
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