2025/05/23 by Shaghayegh Hosseinpour, Hosseinpour, Shaghayegh, Sanchari Das +1 · 1 citation
Decision Sciences · Engineering · Psychology · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Fire dynamics and safety research #Human-Automation Interaction and Safety #Machine Learning (cs.LG) #Risk and Safety Analysis
paper · pdf · doi:10.48550/arxiv.2505.18233
openalex publication_date 2025/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Smishing, or SMS-based phishing, poses an increasing threat to mobile users by mimicking legitimate communications through culturally adapted, concise, and deceptive messages, which can result in the loss of sensitive data or financial resources. In such, we present a multi-channel smishing detection model that combines country-specific semantic tagging, structural pattern tagging, character-level stylistic cues, and contextual phrase embeddings. We curated and relabeled over 84,000 messages across five datasets, including 24,086 smishing samples. Our unified architecture achieves 97.89% accuracy, an F1 score of 0.963, and an AUC of 99.73%, outperforming single-stream models by capturing diverse linguistic and structural cues. This work demonstrates the effectiveness of multi-signal learning in robust and region-aware phishing.