Statistical Decision Making for Authentication and Intrusion Detection

Classification is the problem of categorising data in one of two or more possible classes. In the classical supervised learning framework, examples of each class have already been obtained and the task of the decision maker is to accurately categorise new observations, whose class is unknown. The accuracy is either measured in terms of the rate of misclassification, or in terms of the average cost, for problems where different types of errors carry different costs. In that setting, the problem has three phases: (a) the collection of training data, (b) the estimation of a decision rule based on the training data and (c) the application 1 of the decision rule to new data. Typically, the decision rule remains fixed after the second step.