Novel unsupervised anomaly detection based on robust principal component classifier
基于健壮主成分分类器的无监督异常检测方法研究

Intrusion Detection System(IDS) needs a mass of labeled data in the process of training, which hampers the application and popularity of traditional IDS. Classical principal component analysis is highly sensitive to outliers in training data, and leads to the poor classification accuracy. A novel scheme based on robust principle component classifier was proposed, which obtained principal components that were not influenced much by outliers. An anomaly detection model was constructed from the distance in the principal component space and the reconstruction error of training data. The experiments show that the approach can detect unknown intrusions effectively, and has a good performance in detection rate and false positive rate.