Network Anomaly Detection Based on Semi-supervised Clustering
作者： Tian Shengfeng , Wei Xiaotao , Huang Houkuan
DOI:
关键词: Cluster analysis 、 Intrusion detection system 、 Detection rate 、 Computer science 、 Pattern recognition 、 False positive rate 、 Semi supervised clustering 、 Process (computing) 、 Grid 、 Artificial intelligence 、 Anomaly detection
摘要: A semi-supervised clustering algorithm based on the traditional k-means is proposed for network anomaly detection. We improve original mainly in three aspects. First, number of clusters automatically decided by merging and splitting clusters. Second, a small portion labeled samples are employed to supervise process stage. Also, we modify directly symbolic attribute values. Experimental result KDD 99 intrusion detection datasets shows that our has high rate while maintaining low false positive rate. Key-Words: Network detection, Semi-supervised clustering, Grid-based K-means
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