Continuous user authentication on smartphone devices based on intelligent behavioral biometrics

• Κύριος συγγραφέας: Ορφανού, Έλλη Άννα
• Άλλοι συγγραφείς: Orfanou, Elli Anna
• Γλώσσα: English
• Έκδοση: 2023
• Θέματα: Continuous authentication; Behavioral biometrics; Usable security; Machine learning; Smartphone authentication; Βιομετρικά χαρακτηριστικά; Μηχανική μάθηση; Ταυτοποίηση

The wealth of provided services and personal information that smartphones contain is consistently growing. This fact leads to the demand for secure and usable authentication techniques and for constantly protecting privacy. Continuous Authentication is the process of constantly and unobtrusively verifying the identity of a user during their interaction with a smartphone device, as opposed to a single entry­point authentication. Behavioral biometrics is a security technology that uses patterns in human activities, such as typing or swiping on a device, to authenticate a user’s identity. Smartphone’s integrated sensors are capable of detecting these distinctive behavioral traits of users, based on their individual habits, gestures, and preferences in using their smartphones. In this work, the appliance of Behavioral Biometrics in order to provide Continuous Authentication on smartphones is investigated. An extensive review of the related literature has been conducted and Behavioral Biometric features are divided in six categories: touch gestures, micromotions, keystroke dynamics, walking gait, behavior profiling and fusion of the above. Conclusions were made regarding the robustness of each modality, in order to have a deep understanding of the field and make decisions about the framework proposed. A Continuous Authentication mechanism based on Micromotions is proposed, including a data collector smartphone service that captures measurements of accelerometer, gyroscope and magnetometer smartphone integrated sensors. Data was collected from 12 participants in two physical activities: sitting and walking, in order to simulate a real world environment. After the features pre­processing 60 attributes were extracted and classified using Weka software, employing six machine learning classifiers: Random Forest, SVM, KNN, Multilayer Perceptron, Bayes Network and Naive Bayes. Overall, a maximum accuracy of 96.89% was obtained using Random Forest algorithm for 12 users multi class classification. Moreover, the appliance of the proposed mechanisms in device sharing environments like families was examined, leading to very promising results with an accuracy rate of 99.18% for a 4 users dataset, using only accelerometer and magnetometer sensors. Experimental results show the feasibility of the approach and can provide a foundation for the development of a future real time authentication system