1. A computer implemented information identification method for detecting an attack carried out using irregular data against a classifier that is configured by means of supervised machine learning, the method comprising of: preparing a plurality of pieces of training data each including a feature vector, a class label, and a time stamp of each piece of the training data; generating the classifier by using the feature vector and the class label of the plurality of pieces of training data; clustering the plurality of pieces of training data based on a distance between the feature vectors of the plurality of pieces of training data into a plurality of subclasses; preparing a plurality of pieces of test data each including a feature vector, a class label, and a time stamp of each piece of the test data; classifying the plurality of pieces of test data by using the classifier, the classifier adding the class label to each piece of the test data; clustering the plurality of pieces of test data, which have been classified by using the classifier, into the plurality of subclasses; for each subclass, calculating statistical data representing a ratio of a frequency of the plurality of pieces of test data clustered into the corresponding subclass and a frequency of the plurality of pieces of training data clustered into the corresponding subclass; and warning of a possibility of occurrence of the attack, carried out using the irregular data, in one or more subclasses of the plurality of subclasses, in response to a value of the statistical data calculated for the one or more subclasses of the plurality of subclasses exceeding a predetermined threshold.