Unsupervised Fuzzy Ensembles and Their Use in Intrusion Detection

This paper proposes a novel method for unsupervised ensembles that specifically addresses unbalanced, unsupervised, binary classification problems. Unsupervised learning often experiences the curse of dimensionality, however subspace modeling can overcome this problem. For each subspace created, the classifier produces a decision value. The aggregation of the decision values occurs through the use of fuzzy logic, creating the fuzzy ROC curve. The one-class SVM is utilized for unsupervised classification. The primary source of data for this research is a host based computer intrusion detection dataset.