Researched the unsupervised anomaly detection methods based on clustering analysis, improved the K-means algorithm.

研究了基于聚类分析的非监督式异常检测方法，并改进了K均值算法用于聚类分析。

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Secondly, the anomaly detection model based on K-means algorithm and SOM network is constructed. It can classify the normal and abnormal network data stream so better to detect the unknown attack.

提出了一种k-均值聚类算法和SOM自组织神经网络算法相结合的异常检测模型，使得系统可以更好的分类正常数据流和异常数据流，以此来防范未知的攻击。

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