On the study of anomaly-based spam filtering using spam as representation of normality

In previous work, we presented the first spam filtering method based on anomaly detection that reduces the necessity of labelling spam messages and only employs the representation of legitimate e-mails. This method achieved high accuracy rates detecting spam while maintaining a low false positive rate and reducing the effort produced by labelling spam. In this paper, we study the performance of our previous method when using spam messages to represent normality.