Adaptive Phone Orientation Method for Continuous Authentication Based on Mobile Motion Sensors With the popularity of mobile terminals and the convenience of mobile operating, more and more private data is stored in mobile phones, which makes users pay more attention to mobile security. The data of mobile motion sensors are used to construct the useru0027s behavioral characteristics and biometric characteristics. The principle is to capture the subtle changes in the smartphones caused by the user holding smartphones and touching screens. These changes are unique to different users. This paper studies the effects of mobile phone orientations on continuous authentication based on mobile motion sensors. Additionally, this paper constructs an adaptive phone orientation method for continuous authentication. An orientation detection model is constructed using Kmeans and Random Forest. The authentication model is constructed by using oneclass SVM. Meanwhile, our experiments show that the data of mobile motion sensors are great difference in different phone orientations. We believe considering the situation of multiorientations data affecting authentication accuracy. The adaptive phone orientation method can better fit for the scenarios where users use mobile devices in different phone orientations. Considering the orientation of the data will improve the robustness of the system and the accuracy of authentication.

Authenticating Phone Users Using a GaitBased Histogram Approach on Mobile App Sessions Collectively, userfriendly interfaces, small but impactful sensing technologies, intuitive device designs, and the variety of mobile applications (or apps) have transformed the expectations for cellular phones. Apps are a primary factor in device functionality; they allow users to quickly carry out tasks directly on their device. This paper leverages mobile apps for continuous authentication of mobile device users. We borrow from a gaitbased approach by continuously extracting nbin histograms from numerically encoded app data. Since more active subjects will generate more data, it would be trivial to distinguish between these subjects and others which are not as active. Thus, we divided a dataset of 19 months of app data from 181 subjects into three datasets to determine if minimally active, moderately active, or very active subjects were more challenging to authenticate. Using the absolute distance between two histograms, our approach yielded a worstcase EER of 0.188 and a bestcase EER of 0.036 with a worstcase initial training period of 1.06 hours. We also show a positive correlation between user activity level and performance, and template size and performance. Our method is characterized by minimal training samples and a contextindependent evaluation, addressing important factors which are known to affect the practicality of continuous authentication systems.