SoK: A Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments

TL;DR

This paper systematically reviews 41 studies on adaptive authentication in mobile environments, highlighting the prevalent use of machine learning and AI techniques like anomaly detection and spatio-temporal analysis for enhanced security and usability.

Contribution

It provides a comprehensive analysis of existing adaptive authentication methods, identifying trends, challenges, and the increasing role of AI and sensor data in mobile security.

Findings

High reliance on machine learning (64.3%) in adaptive authentication

Predominant use of anomaly detection (57.1%) and spatio-temporal analysis (52.4%)

Growing integration of sensor-based and location-aware models

Abstract

As mobile computing becomes central to digital interaction, researchers have turned their attention to adaptive authentication for its real-time, context- and behavior-aware verification capabilities. However, many implementations remain fragmented, inconsistently apply intelligent techniques, and fall short of user expectations. In this Systematization of Knowledge (SoK), we analyze 41 peer-reviewed studies since 2011 that focus on adaptive authentication in mobile environments. Our analysis spans seven dimensions: privacy and security models, interaction modalities, user behavior, risk perception, implementation challenges, usability needs, and machine learning frameworks. Our findings reveal a strong reliance on machine learning (64.3%), especially for continuous authentication (61.9%) and unauthorized access prevention (54.8%). AI-driven approaches such as anomaly detection (57.1%) and spatio-temporal analysis (52.4%) increasingly shape the interaction landscape, alongside growing use of sensor-based and location-aware models.