Analysing Shoulder Surfing Attacks on Biometrics Continuous Authentication

AI Approach - These days, shoulder surfing attacks are very common. Sometimes these attacks are unintentional and without any adverse
intention to steal any sensitive information. Yet, they are lethal with or without any ill intentions to obtain any sensitive data. There
are studies in which these attacks are addressed and relevant proposals are present to provide robust countermeasures. However,
with the increase use of smart devices, shoulder surfing attacks are becoming a challenging security concern. This project is to
address this issue and to integrate the fast growing AI/ML techniques to examine and propose an effective and secure biometric
continuous authentication framework that can be implemented on smart devices to protect the user sensitive information from
these attacks. This study is going to perform a detailed security and privacy analysis of the existing biometrics-based continuous
authentication schemes using machine learning and AI based algorithms, such as, kNN, K-Means and Random Forest. The
security risk analysis is investigated using Multi-Criteria Decision Analysis (MCDA) system. The offered risk analysis is further
utilized to countermeasure the shoulder surfing attacks and other relevant security attacks by proposing and developing a more
robust and privacy preserving biometric continuous authentication framework. The BAN or SVO logical analysis is going to be used
to provide a logical security analysis and the framework is going to be implemented using Scyther or Tamarin Prover to provide an
automated security analysis.