Mogura Taiji Eτ – A Paraconsistent Annotated Evidential Logic Eτ approach to enhance WAF

As web applications become increasingly central to digital infrastructure, they also represent prime targets for sophisticated adversarial attacks. Traditional Web Application Firewalls, which rely on binary or statistical classification mechanisms, often fail under conditions of ambiguity—particularly when facing mutated payloads designed to evade detection. This study proposes Mogura Taiji Eτ , a model that applies Paraconsistent Annotated Evidential Logic Eτ to enhance detection by explicitly managing contradictory and uncertain inputs. The model was evaluated using a labeled synthetic dataset. Results demonstrate significant improvements in detection reliability, with marked reductions in classification errors and increased consistency in decision-making. Beyond quantitative gains, the model exhibits a qualitative shift in behavior, enabling more resilient and interpretable threat classification under adversarial conditions. This work contributes to the scientific field by advancing non-classical logic in computational security, offers a practical solution for the cybersecurity industry, and supports societal trust in digital systems. It aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure) and SDG 16 (Peace, Justice and Strong Institutions).