A Secure Multi-Factor Authentication Framework for Digital Traffic Offense Management Systems

Classic traffic law enforcement infrastructure in Nigeria is based primarily on single-factor authentication (SFA) strategies, exposing sensitive traffic violation records to data breaches, credential attacks, and tampering. In this work, a scalable Multi-Factor Authentication (MFA)-oriented architecture for enhancing the confidentiality, integrity, and availability of traffic violation databases is proposed and developed. Knowledge-based (password), possession-based (one-time passcode), and inherence-based (biometric) authentication levels are integrated in a distributed web application framework developed with Django, ReactJS, and PostgreSQL. Following the Design Science Research (DSR) paradigm, the artifact was developed, tested, and validated through functional, security, and scalability testing following OWASP and ISO/IEC 27001 standards. Experimental results showed 99.6% security effectiveness, 97.4% authentication accuracy, and 99.7% system availability under concurrent load conditions. The system was completely resistant to emulated cyberattacks in the form of brute-force, and SQL injection and maintained data consistency through replicated PostgreSQL clustering and tamper-proof auditing. Findings confirm that the integration of MFA and distributed architecture substantially improves data reliability, traceability, and user accountability in digital law enforcement systems. The study makes theoretical and practical contributions through the confirmation of MFA as an extensible and sustainable model for secure e-governance applications in developing economies.