Behavioral Monitoring on Smartphones for Intrusion Detection in Web Systems : A Study of Limitations and Applications of Touchscreen Biometrics
Abstract: Touchscreen biometrics is the process of measuring user behavior when using a touchscreen, and using this information for authentication. This thesis uses SVM and k-NN classifiers to test the applicability of touchscreen biometrics in a web environment for smartphones. Two new concepts are introduced: model training using the Local Outlier Factor (LOF), as well as building custom models for touch behaviour in the context of individual UI components instead of the whole screen. The lowest error rate achieved was 5.6 \% using the k-NN classifier, with a standard deviation of 2.29 \%. No real benefit using the LOF algorithm in the way presented in this thesis could be found. It is found that the method of using contextual models yields better performance than looking at the entire screen. Lastly, ideas for using touchscreen biometrics as an intrusion detection system is presented.
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