Evaluating Classifiers for Mobile-Masquerader Detection

As a result of the impersonation of a user of a mobile terminal, sensitive information kept locally or accessible over the network can be abused. The means of masquerader detection are therefore needed to detect the cases of impersonation. In this paper, the problem of mobile-masquerader detection is considered as a problem of classifying the user behaviour as originating from the legitimate user or someone else. Different behavioural characteristics are analysed by designated one-class classifiers whose classifications are combined. The paper focuses on selecting the classifiers for mobile-masquerader detection. The selection process is conducted in two phases. First, the classification accuracies of classifiers are empirically evaluated, and inaccurate classifiers are excluded. After that, the accuracies of different classifier combinations are explored, and the combination with the best classification accuracy is identified. The experimental results suggest that, in order to achieve better accuracy, the individual classifiers with both high classification accuracy and a small number of non-classifications need to be selected.