1. A method comprising: collecting a first computer process related dataset comprising a plurality of normal temporal event sequences; learning, using the first computer process related dataset, a one-class sequence classifier f(x) that obtains a decision boundary configured to label temporal event sequences; collecting a second computer process related dataset comprising at least one new temporal event sequence; and evaluating the at least one new temporal event sequence using the decision boundary to label the at least one new temporal event sequence as an abnormal sequence, causing a computer system to issue an alert, wherein learning the classifier learning comprises: randomly initializing a solution space (Ω); constructing an undirected graph for the normal temporal event sequences in the first computer process related dataset; capturing at least one temporal dynamic of the normal temporal event sequences of the first computer process related dataset; assigning labels to each of the normal temporal event sequences of the first computer process related dataset by computing, for each of the normal temporal event sequences a probability that the at least one normal temporal event sequence of the first computer process related dataset is a normal sequence or an abnormal sequence, wherein at least one of the at least one normal temporal event sequences is labeled as an abnormal sequence; and refining the classifier using the plurality of normal temporal event sequences of the first computer process related dataset with respective labels.