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

2025-10-04

AI Summary

Rising computational capabilities have rendered conventional cryptographic mechanisms increasingly complex, undermining user compliance and escalating administrative risks. To address this, we propose an adaptive multi-factor authentication (MFA) framework that integrates password structure analysis, behavioral modeling, and contextual awareness—including device, network, and geolocation features—to enable dynamic policy orchestration. Our key contribution is a user-profile-driven co-optimization of password policies, jointly enhancing security assurance and usability. Experimental evaluation demonstrates that the framework blocks over 99.8% of unauthorized access attempts—exceeding the security guarantees of NIST SP 800-63B—while reducing average user interaction overhead by 42% and maintaining system adaptive response latency below 200 ms.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Security

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Consent frameworks and practices on the web

Abstract

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.

Problem

• Addresses weak password management and user compliance issues
• Combats evolving computational threats to password security systems
• Integrates behavioral and contextual factors for adaptive authentication

Innovation

• Multi-layered behavioral and credential analysis framework
• Learning-based models construct detailed user profiles
• Adaptive authentication integrates dynamic policy mechanisms