An unsupervised anomaly detection patterns learning algorithm

Most anomaly detection patterns learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. In this paper, we present an unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage.