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An Efficient Hybrid Clustering-PSO Algorithm for Anomaly Intrusion Detection
2011
Journal of Software
Generally speaking, in anomaly intrusion detection, modeling the normal behavior of activities performed by a user or a program is an important issue. Currently most machine-learning algorithms which are widely used to establish user's normal behaviors need labeled data for training first, so they are computational expensive and sometimes misled by artificial data. This study proposes a PSO-based optimized clustering method IDCPSO for modeling the normal patterns of a user's activities which
doi:10.4304/jsw.6.12.2350-2360
fatcat:o47uwgtmhrgipmlyvcn7ztpv5y