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EXAMINING OUTLIER DETECTION PERFORMANCE FOR PRINCIPAL COMPONENTS ANALYSIS METHOD AND ITS

Intrusion detection has gasped the attention of both commercial institutions and academic research area. In this paper PCA (Principal Components Analysis) was utilized as unsupervised technique to detect multivariate outliers on the dataset of an hour duration of time. PCA is sensitive to outliers since it depend on non-robust estimators. This lead us using MCD (Minimum Covariance Determinant) and PP (Projection Pursuit) as two different robustification techniques for the PCA. The results obtained from experiments show that PCA generates a high false alarms due to masking and swamping effects, while MCD and PP detection rate is much accurate and both reveals the effects of masking and swamping undergo the PCA method.