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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21101 |
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| _version_ | 1866916867906469888 |
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| author | Podapati, Vyoma Harshitha Nigam, Divyansh Das, Sanchari |
| author_facet | Podapati, Vyoma Harshitha Nigam, Divyansh Das, Sanchari |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoK: A Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments Podapati, Vyoma Harshitha Nigam, Divyansh Das, Sanchari Cryptography and Security 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. |
| title | SoK: A Systematic Review of Context- and Behavior-Aware Adaptive Authentication in Mobile Environments |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.21101 |