BubbleMap: Privilege Mapping for Behavior-Based Implicit Authentication Systems

Leveraging users' behavioral data sampled by various sensors during the identification process, implicit authentication (IA) relieves users from explicit actions such as remembering and entering passwords. Various IA schemes have been proposed based on different behavioral and contextual features such as gait, touch, and GPS. However, existing IA schemes suffer from false positives, i.e., falsely accepting an adversary, and false negatives, i.e., falsely rejecting the legitimate user due to users' behavior change and noise. To deal with this problem, we propose BubbleMap (BMap), a framework that can be seamlessly incorporated into any existing IA system to balance between security (reducing false positives) and usability (reducing false negatives) as well as reducing the equal error rate (EER). To evaluate the proposed framework, we implemented BMap on five state-of-the-art IA systems. We also conducted an experiment in a real-world environment from 2016 to 2020. Most of the experimental results show that BMap can greatly enhance the IA schemes' performances in terms of the EER, security, and usability, with a small amount of penalty on energy consumption.

Automated summary

By leveraging users' behavioral data sampled by various sensors, implicit authentication (IA) eliminates the need for explicit actions such as remembering and entering passwords. However, existing IA schemes face challenges of false positives and false negatives due to users' behavior changes and noise. To address this, we propose BubbleMap (BMap), a framework that enhances the performance of IA systems in terms of security, usability, and reducing the equal error rate (EER). Evaluations show that BMap significantly improves the performance of IA schemes with minimal impact on energy consumption.