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

Tonmoy Ghosh

TL;DR

AdaptAuth targets the enduring gap between password security and usability by fusing a Password Dissection Mechanism with a Dynamic Password Policy, augmented by a rich set of 173 behavioral and credential features. The approach relies on device fingerprinting, geolocation, temporal patterns, and contextual signals to build individualized user profiles and robust decision-making, aiming to resist brute-force, credential-stuffing, and related attacks while reducing false lockouts. Its core innovations include a reworked block-based password dissection with a formal match metric $M_p = (M_v / P_o) \times 100$ and an adaptive time-based rule (Time Rule) that binds password logic to login timing. While currently conceptual, AdaptAuth envisions a scalable, user-centric authentication ecosystem with anticipated empirical validation, privacy considerations, and potential for centralized cross-platform deployment.

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.

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

TL;DR

AdaptAuth targets the enduring gap between password security and usability by fusing a Password Dissection Mechanism with a Dynamic Password Policy, augmented by a rich set of 173 behavioral and credential features. The approach relies on device fingerprinting, geolocation, temporal patterns, and contextual signals to build individualized user profiles and robust decision-making, aiming to resist brute-force, credential-stuffing, and related attacks while reducing false lockouts. Its core innovations include a reworked block-based password dissection with a formal match metric and an adaptive time-based rule (Time Rule) that binds password logic to login timing. While currently conceptual, AdaptAuth envisions a scalable, user-centric authentication ecosystem with anticipated empirical validation, privacy considerations, and potential for centralized cross-platform deployment.

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.

Paper Structure

This paper contains 44 sections, 6 equations, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Brute Force Attacks
  4. Dictionary Attacks
  5. Shoulder Surfing Attacks
  6. Credential Stuffing
  7. Password Dissection Mechanism
  8. Dissection and percentage matching
  9. Keyboard layout consideration
  10. Record of login attempt history
  11. Dynamic Password Policy Mechanism
  12. Caesar Cipher Rule
  13. Space Rule
  14. Leet Code Rule
  15. Special Character Rule
  16. ...and 29 more sections

Figures (12)

  • Figure 1: Delineating the password dilemma where easy passwords lead to account compromise, account compromise leads to strong passwords, strong password leads to password forgettability, password forgettability leads to easy passwords, and the cycle continues.
  • Figure 2: Depicting the Reworked Password Dissection Mechanism.
  • Figure 3: Keyboard layout mechanism.
  • Figure 4: Illustration of the Caesar Cipher Rule Mechanism.
  • Figure 5: Depiction of Space Rule.
  • ...and 7 more figures