# AdaptAuth: Multi-Layered Behavioral and Credential Analysis for a Secure and Adaptive Authentication Framework for Password Security

arXiv: https://arxiv.org/abs/2510.09645 · HTML: https://mlsanity.com/paper/2510.09645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 155 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [IFTT-PIN: A Self-Calibrating PIN-Entry Method](https://mlsanity.com/paper/2407.02269/) (2024)
- [Optimizing Password Composition Policies](https://mlsanity.com/paper/1302.5101/) (2013)
- [Text Entry Method Affects Password Security](https://mlsanity.com/paper/1403.1910/) (2014)
- [DALock: Distribution Aware Password Throttling](https://mlsanity.com/paper/2005.09039/) (2020)
- [SoK: Privacy Personalised -- Mapping Personal Attributes & Preferences of Privacy Mechanisms for Shoulder Surfing](https://mlsanity.com/paper/2411.18380/) (2024)
- [The 23andMe Data Breach: Analyzing Credential Stuffing Attacks, Security Vulnerabilities, and Mitigation Strategies](https://mlsanity.com/paper/2502.04303/) (2025)

## Abstract (arXiv)

Password security has been compelled to evolve in response to the growing computational capabilities of modern systems. However, this evolution has often resulted in increasingly complex security practices that alienate users, leading to poor compliance and heightened vulnerability. Consequently, individuals remain exposed to attackers through weak or improperly managed passwords, underscoring the urgent need for a comprehensive defense mechanism that effectively addresses password-related risks and threats. In this paper, we propose a multifaceted solution designed to revolutionize password security by integrating diverse attributes such as the Password Dissection Mechanism, Dynamic Password Policy Mechanism, human behavioral patterns, device characteristics, network parameters, geographical context, and other relevant factors. By leveraging learning-based models, our framework constructs detailed user profiles capable of recognizing individuals and preventing nearly all forms of unauthorized access or device possession. The proposed framework enhances the usability-security paradigm by offering stronger protection than existing standards while simultaneously engaging users in the policy-setting process through a novel, adaptive approach.