CyberText Shield: AI-Powered Protection for Secure and Scam-Free Messages

Abstract

The rapid proliferation of mobile communication has established smishing as a primary vector for cyber-social attacks, leveraging user trust to facilitate unauthorized data exfiltration and malware delivery. Conventional defensive mechanisms, including static keyword filters and sender blacklists, increasingly struggle with high false-positive rates and fail to neutralize sophisticated adversarial permutations. To address these limitations, we propose CyberText Shield, a specialized mobile-centric framework for real-time smishing detection. At its technical core, the system transitions from traditional sequential text analysis to high-order relational modeling via Hypergraph Neural Networks (HGNN). By representing message tokens, sender metadata, and contextual features as nodes connected through an incidence-based hypergraph structure, the model captures non-linear dependencies that standard deep learning architectures frequently overlook. We implement an edge-optimized HGNN inference engine tailored for resource-constrained hardware, achieving a detection accuracy of 91% with a critical processing latency of less than 100ms per message.This integration of lightweight graph-based learning with a user-centric educational interface provides a robust, interpretable, and scalable defense against the evolving landscape of mobile phishing threats. Experimental results validate the system's effectiveness in balancing computational efficiency with superior predictive performance in real-world deployment scenarios.