Behavior-Based Authentication for Inclusive Digital Public Services

Daniel Foster

Behavior-Based Authentication for Inclusive Digital Public Services

Author(s)	Dr. Daniel Foster
Country	United States
Abstract	Digital public services increasingly require secure authentication for access to taxation, licensing, social benefits, healthcare administration, education, identity records, and other government functions. Conventional authentication based on passwords, manually transcribed one-time codes, and repeated challenges can create substantial usability and accessibility burdens, particularly when users experience cognitive, motor, sensory, device, connectivity, or digital-literacy constraints. Behavior-based authentication offers a complementary approach by evaluating interaction patterns such as keystroke cadence, pointer movement, touch behavior, device motion, navigation timing, and session characteristics after a legitimate user has entered a service. Current NIST guidance explicitly recognizes biological and behavioral characteristics within biometrics and allows session monitoring to consider behavioral signals such as typing cadence. However, NIST does not support biometrics as a standalone authentication factor under its current federal authentication requirements; biometric authentication is limited to use with a physical authenticator, behavioral and biometric data require privacy safeguards, and an alternative non-biometric authentication option must always be provided. WCAG 2.2 similarly emphasizes accessible authentication, requiring an authentication path that does not impose unsupported cognitive-function tests and encouraging alternatives that reduce memory and transcription burdens. This study develops a privacy-aware, behavior-assisted authentication architecture for inclusive digital public services. The proposed approach does not treat behavioral biometrics as an invisible sole gatekeeper. Instead, behavioral evidence contributes to an adaptive session-risk score following an initial cryptographic or otherwise appropriate authentication event. A synthetic dataset representing 600 citizen profiles and 24,000 authentication sessions was modeled across four architectures: password-plus-OTP authentication, conventional risk-adaptive authentication, behavior-assisted continuous session monitoring, and an inclusive hybrid architecture integrating phishing-resistant authentication where available, adaptive behavioral monitoring, local or minimized feature processing, accessible alternative authentication, and proportionate step-up verification. The simulated hybrid architecture produced the highest risk-and-impostor detection score at 93 out of 100 while reducing the user-burden score to 31 and increasing inclusive successful access to 93%. Behavior-assisted monitoring also reduced repeated authentication interruptions compared with the conventional password-plus-OTP baseline. The results are methodological illustrations rather than empirical findings. They support a broader design argument that inclusive authentication should optimize security, usability, accessibility, privacy, and recoverability simultaneously. Behavior-based signals are most defensible when they silently increase confidence during ordinary sessions and trigger proportionate reauthentication when anomalous behavior appears, rather than directly terminating essential public-service access. The study concludes that digital governments should adopt behavior-informed authentication rather than behavior-dependent authentication, preserving cryptographic security, user choice, accessible fallback, transparent risk governance, privacy protection, and human-assisted recovery as essential components of inclusive public-service identity systems.
Keywords	behavioral biometrics, digital public services, continuous authentication, inclusive authentication, digital identity, accessibility, adaptive authentication, public-sector cybersecurity, behavioral analytics, digital government
Field	Engineering
Published In	Volume 3, Issue 3, May-June 2022
Published On	2022-05-04

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Behavior-Based Authentication for Inclusive Digital Public Services

Share this