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As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.
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Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively. © 2020 IEEE.
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