Analysis of interaction trace maps for active authentication on smart devices

The availability and affordability of handheld smart devices have made life easier by enabling us to do work on the go. Their widespread use brings with it concerns relating to data security and privacy. The rising demand to secure private and highly confidential data found on smart devices has motivated researchers to devise means for ensuring privacy and security at all times. This kind of continuous user authentication scheme would add an additional layer of much needed security to smart devices. In this context, touch screen interactions have recently been studied as an effective modality to perform active user authentication on mobile devices. In this paper, a visual analysis based active authentication framework has been presented. Considering the touch screen as a canvas, interaction trace maps are constructed as a result of user interactions within various applications. The user touch gestures are captured and represented as drawing strokes on the canvas. The behavioral and physiological characteristics of users are modeled as signatures by combining texture and shape features from the interaction trace maps. A two-step mechanism with support vector machines exploit this signature to perform active user authentication. Experiments conducted with various datasets show that the proposed framework compares favorably with other state-of-the-art methods.